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Have you ever wondered how businesses keep their huge collections of images organized and easy to find? The secret is Image Tagging APIs. Managing images well can greatly improve your business processes in today’s world, where visual content is everywhere. This blog will discuss how Image Tagging APIs work and their importance. First, we’ll examine how image tagging with AI is better than doing it by hand. Then, we’ll explain how these APIs work and what technology they use. We will also point out the key features you should look for when choosing an API to make the best decision. Next, we’ll explore the practical uses of Image Tagging APIs in different industries. We will also be showing their wide-ranging benefits. We’ll offer tips on what to consider to help you pick the suitable API. Lastly, we’ll share best practices for using these APIs to get the most out of them. Let’s learn how Image Tagging APIs can change how you handle images and boost your business. What Is the Power of Image Tagging Beyond Manual Labeling? Manual image tagging takes a lot of time and effort. People have to look at each image and assign tags one by one. This process is slow and prone to mistakes. Keeping tags accurate and consistent gets harder as the number of images grows. It also can’t keep up with the fast pace businesses need today. Therefore, leading to delays and inefficiencies. Image Tagging APIs Automated Image Tagging APIs use artificial intelligence (AI) to automate image labeling. These tools can look at images, recognize objects and scenes, and assign the right image tags. Here are some benefits: 👉Automated tagging is much faster than manual tagging. 👉AI reduces human error, making tags more accurate. 👉APIs can handle large numbers of images quickly. 👉Automation cuts down the need for human labor, saving money. Use Cases Across Industries Image Tagging APIs are useful in many fields. Here are some examples: 👉Online stores use image tagging to organize products. Tags like color, size, and style help customers find what they want. Amazon tags products to improve search results and recommendations. 👉Media companies tag images to manage their vast libraries. This helps them find and organize content quickly. Netflix tags images to suggest relevant shows and movies to viewers. 👉Schools and educational platforms tag images to sort materials. This makes it easier for students and teachers to find resources. Khan Academy tags videos by subject and grade level. 👉Hospitals tag medical images like X-rays and MRIs to organize patient records. This helps doctors diagnose and treat patients faster. Hospitals use image tagging to manage medical records efficiently. How Do Image Tagging APIs Work? Image tagging APIs help organize and label pictures using smart technology. Furthermore, they use AI techniques like object recognition to identify items in a photo. For example, the API can recognize the sand, water, and people in a beach photo. Scene understanding helps the API see the bigger picture. It not only identifies objects but also understands their context. Furthermore, this makes tagging more accurate. The new Filestack EyeQ update improves image quality, helping the AI work better. Metadata is data about data. For images, it includes tags, descriptions, and categories. Moreover, this makes finding and organizing pictures easier and faster. “Computer vision” is a field that enables computers to see and understand images. “Image classification” is part of this field. It means sorting images into different groups based on their content. These technologies make image tagging APIs powerful and useful for managing photos. Let us give you an example with Filestack image captioning. Suppose we have the below image: We can get the tags for this image using the below Filestack URL: https://cdn.filestackcontent.com/<FILESTACK_API_KEY>/security=p:<POLICY>,s:<SIGNATURE>/tags/<EXTERNAL_URL> After adding the API Key, signature, and policy, here is the response: {
  "tags": {
    "auto": {
      "adult": 98,
      "architecture": 99,
      "brick": 99,
      "building": 99,
      "clothing": 88,
      "face": 99,
      "happy": 87,
      "head": 99,
      "knitwear": 88,
      "long sleeve": 56,
      "male": 98,
      "man": 98,
      "pants": 55,
      "person": 99,
      "photography": 99,
      "portrait": 99,
      "sitting": 99,
      "sleeve": 57,
      "smile": 87,
      "sweater": 88,
      "sweatshirt": 67,
      "wall": 99
    },
    "user": null
  }
} What Are the Key Features to Look for in an Image Tagging API? When choosing an image tagging API, look for these key features: ✔️The API should correctly identify and tag images with a higher accuracy. ✔️It should quickly handle large numbers of images. ✔️The API should grow with your image library. ✔️It should allow adjustments for your specific needs. In other words, it should have better customization features. ✔️The API should work well with your current content management systems (CMS) and digital asset management (DAM) platforms. ✔️Make sure the pricing is reasonable and fits your budget. What Are the Practical Applications of Image Tagging APIs? Image tagging APIs have many useful applications: ✔️Accurate tags make images easier to find on search engines by boosting the SEO. Therefore improving website visibility. ✔️Tags help quickly find specific images in a media library or website. ✔️The API automatically sorts and categorizes images. Furthermore, reducing manual work. ✔️Tags describe images for visually impaired users. Hence making websites more inclusive. ✔️By analyzing image content, the API can recommend personalized content to users. How Do You Choose the Right Image Tagging API for Your Needs? Choosing the right image tagging API depends on your needs and budget. Here are some popular options: 1. Google Cloud Vision Known for high accuracy and features like text and landmark detection. Besides, it works well with other Google Image Tagger software services but can be pricey for large-scale use. 2. Amazon Rekognition This image tagging software automates service and offers strong image and video analysis, including facial recognition. Moreover, it scales well and integrates with other AWS services. However, costs can add up with heavy use. 3. Clarifai User-friendly with customizable models for tagging images. Moreover, it’s fast and easy to use but might not be as accurate in complex cases as Google or Amazon. When choosing the best image tagging work API for you, consider these points: Higher Accuracy
Advanced Features
Easy Integration
Higher Scalability
Affordable Cost What Are the Best Practices for Implementing Image Tagging APIs? Implementing an image tagging API can improve your image management. Here are some best practices: Name your images consistently to find them easily.
Use clear, high-quality images for better tagging accuracy.
Organize images into folders or groups to make tagging easier for personal photo libraries. Integrating the API with Your Workflows Choose an API that works well with your CMS or digital asset management (DAM) platforms.
Automate the tagging process to save time and ensure consistency.
Tag large numbers of images at once to increase efficiency to upload images. Data Privacy and Security 🛡️Make sure the API follows data privacy laws like GDPR or CCPA to protect user data. 🛡️Use encryption to keep image data safe during transfer and storage. 🛡️Limit who can access and change image data to improve security. Conclusion Automatic Image Tagging APIs help businesses manage large collections of images quickly and accurately. Moreover, these APIs use AI to tag images. Besides, they are saving time and reducing mistakes compared to manual tagging. In this blog, we learned: 👉How Automated Picture Tagging APIs work and why they are important. 👉Image tagging APIs’ key features 👉How they are used in different industries 👉Tips for choosing the right one for your needs and budget. 👉Best practices for getting the most out of these APIs. Using Image Tagging APIs can improve your SEO, make finding images easier, automate organization, and help with accessibility. Moreover, they also allow personalized content recommendations. Hence making your site or app more engaging through relevant tags. FAQs What is an images tagging process? The automatically tag images tagging process involves labeling images with descriptive tags using AI. Can Filestack help with the image tagging process faster? Yes. Filestack speeds up the image tagging process effectively. Is Filestack secure enough to be trusted for images tagging APIs? Filestack is secure and trustworthy for image tagging APIs due to its higher security strategies. Have you ever wondered how businesses keep their huge collections of images organized and easy to find? The secret is Image Tagging APIs. collections of images Image Tagging Managing images well can greatly improve your business processes in today’s world, where visual content is everywhere. This blog will discuss how Image Tagging APIs work and their importance. First, we’ll examine how image tagging with AI is better than doing it by hand. Then, we’ll explain how these APIs work and what technology they use. We will also point out the key features you should look for when choosing an API to make the best decision. Next, we’ll explore the practical uses of Image Tagging APIs in different industries. We will also be showing their wide-ranging benefits. We’ll offer tips on what to consider to help you pick the suitable API. Lastly, we’ll share best practices for using these APIs to get the most out of them. Let’s learn how Image Tagging APIs can change how you handle images and boost your business. What Is the Power of Image Tagging Beyond Manual Labeling? Manual image tagging takes a lot of time and effort. People have to look at each image and assign tags one by one. This process is slow and prone to mistakes. Keeping tags accurate and consistent gets harder as the number of images grows. It also can’t keep up with the fast pace businesses need today. Therefore, leading to delays and inefficiencies. Image Tagging APIs Automated Image Tagging APIs use artificial intelligence (AI) to automate image labeling . These tools can look at images, recognize objects and scenes, and assign the right image tags. Here are some benefits: automate image labeling 👉Automated tagging is much faster than manual tagging. 👉AI reduces human error, making tags more accurate. 👉APIs can handle large numbers of images quickly. 👉Automation cuts down the need for human labor, saving money. Use Cases Across Industries Image Tagging APIs are useful in many fields. Here are some examples: 👉Online stores use image tagging to organize products. Tags like color, size, and style help customers find what they want. Amazon tags products to improve search results and recommendations. 👉Media companies tag images to manage their vast libraries. This helps them find and organize content quickly. Netflix tags images to suggest relevant shows and movies to viewers. 👉Schools and educational platforms tag images to sort materials. This makes it easier for students and teachers to find resources. Khan Academy tags videos by subject and grade level. 👉Hospitals tag medical images like X-rays and MRIs to organize patient records. This helps doctors diagnose and treat patients faster. Hospitals use image tagging to manage medical records efficiently. How Do Image Tagging APIs Work? Image tagging APIs help organize and label pictures using smart technology. Furthermore, they use AI techniques like object recognition to identify items in a photo. For example, the API can recognize the sand, water, and people in a beach photo. Scene understanding helps the API see the bigger picture. It not only identifies objects but also understands their context. Furthermore, this makes tagging more accurate. The new Filestack EyeQ update improves image quality, helping the AI work better. Metadata is data about data. For images, it includes tags, descriptions, and categories. Moreover, this makes finding and organizing pictures easier and faster. “Computer vision” is a field that enables computers to see and understand images. “Image classification” is part of this field. It means sorting images into different groups based on their content. These technologies make image tagging APIs powerful and useful for managing photos. Let us give you an example with Filestack image captioning. Suppose we have the below image: We can get the tags for this image using the below Filestack URL: https://cdn.filestackcontent.com/<FILESTACK_API_KEY>/security=p:<POLICY>,s:<SIGNATURE>/tags/<EXTERNAL_URL> https://cdn.filestackcontent.com/<FILESTACK_API_KEY>/security=p:<POLICY>,s:<SIGNATURE>/tags/<EXTERNAL_URL> After adding the API Key, signature, and policy, here is the response: {
  "tags": {
    "auto": {
      "adult": 98,
      "architecture": 99,
      "brick": 99,
      "building": 99,
      "clothing": 88,
      "face": 99,
      "happy": 87,
      "head": 99,
      "knitwear": 88,
      "long sleeve": 56,
      "male": 98,
      "man": 98,
      "pants": 55,
      "person": 99,
      "photography": 99,
      "portrait": 99,
      "sitting": 99,
      "sleeve": 57,
      "smile": 87,
      "sweater": 88,
      "sweatshirt": 67,
      "wall": 99
    },
    "user": null
  }
} {
  "tags": {
    "auto": {
      "adult": 98,
      "architecture": 99,
      "brick": 99,
      "building": 99,
      "clothing": 88,
      "face": 99,
      "happy": 87,
      "head": 99,
      "knitwear": 88,
      "long sleeve": 56,
      "male": 98,
      "man": 98,
      "pants": 55,
      "person": 99,
      "photography": 99,
      "portrait": 99,
      "sitting": 99,
      "sleeve": 57,
      "smile": 87,
      "sweater": 88,
      "sweatshirt": 67,
      "wall": 99
    },
    "user": null
  }
} What Are the Key Features to Look for in an Image Tagging API? When choosing an image tagging API, look for these key features: ✔️The API should correctly identify and tag images with a higher accuracy. ✔️It should quickly handle large numbers of images. ✔️The API should grow with your image library. ✔️It should allow adjustments for your specific needs. In other words, it should have better customization features. ✔️The API should work well with your current content management systems (CMS) and digital asset management (DAM) platforms. ✔️Make sure the pricing is reasonable and fits your budget. What Are the Practical Applications of Image Tagging APIs? Image tagging APIs have many useful applications: ✔️Accurate tags make images easier to find on search engines by boosting the SEO. Therefore improving website visibility. ✔️Tags help quickly find specific images in a media library or website. ✔️The API automatically sorts and categorizes images. Furthermore, reducing manual work. ✔️Tags describe images for visually impaired users. Hence making websites more inclusive. ✔️By analyzing image content, the API can recommend personalized content to users. How Do You Choose the Right Image Tagging API for Your Needs? Choosing the right image tagging API depends on your needs and budget. Here are some popular options: 1. Google Cloud Vision Google Cloud Vision Known for high accuracy and features like text and landmark detection. Besides, it works well with other Google Image Tagger software services but can be pricey for large-scale use. 2. Amazon Rekognition Amazon Rekognition This image tagging software automates service and offers strong image and video analysis, including facial recognition. Moreover, it scales well and integrates with other AWS services. However, costs can add up with heavy use. 3. Clarifai Clarifai User-friendly with customizable models for tagging images. Moreover, it’s fast and easy to use but might not be as accurate in complex cases as Google or Amazon. When choosing the best image tagging work API for you, consider these points: Higher Accuracy Advanced Features Easy Integration Higher Scalability Affordable Cost Higher Accuracy Higher Accuracy Advanced Features Advanced Features Easy Integration Easy Integration Higher Scalability Higher Scalability Affordable Cost Affordable Cost What Are the Best Practices for Implementing Image Tagging APIs? Implementing an image tagging API can improve your image management. Here are some best practices: Name your images consistently to find them easily. Use clear, high-quality images for better tagging accuracy. Organize images into folders or groups to make tagging easier for personal photo libraries. Name your images consistently to find them easily. Use clear, high-quality images for better tagging accuracy. Organize images into folders or groups to make tagging easier for personal photo libraries. Integrating the API with Your Workflows Choose an API that works well with your CMS or digital asset management (DAM) platforms. Automate the tagging process to save time and ensure consistency. Tag large numbers of images at once to increase efficiency to upload images. Choose an API that works well with your CMS or digital asset management (DAM) platforms. Automate the tagging process to save time and ensure consistency. Tag large numbers of images at once to increase efficiency to upload images. Data Privacy and Security 🛡️Make sure the API follows data privacy laws like GDPR or CCPA to protect user data. 🛡️Use encryption to keep image data safe during transfer and storage. 🛡️Limit who can access and change image data to improve security. Conclusion Automatic Image Tagging APIs help businesses manage large collections of images quickly and accurately. Moreover, these APIs use AI to tag images. Besides, they are saving time and reducing mistakes compared to manual tagging. In this blog, we learned: 👉How Automated Picture Tagging APIs work and why they are important. 👉Image tagging APIs’ key features 👉How they are used in different industries 👉Tips for choosing the right one for your needs and budget. 👉Best practices for getting the most out of these APIs. Using Image Tagging APIs can improve your SEO, make finding images easier, automate organization, and help with accessibility. Moreover, they also allow personalized content recommendations. Hence making your site or app more engaging through relevant tags. FAQs What is an images tagging process? The automatically tag images tagging process involves labeling images with descriptive tags using AI. Can Filestack help with the image tagging process faster? Yes. Filestack speeds up the image tagging process effectively. Is Filestack secure enough to be trusted for images tagging APIs? Filestack is secure and trustworthy for image tagging APIs due to its higher security strategies.

Streamline Your Visual Content with Advanced Image Tagging APIs

উন্নত ইমেজ ট্যাগিং API এর সাথে আপনার ভিজ্যুয়াল কন্টেন্ট স্ট্রীমলাইন করুন

Optimieren Sie Ihren visuellen Inhalt mit erweiterten Bild-Tagging-APIs

Optimice su contenido visual con API de etiquetado de imágenes avanzadas

Rationalisez votre contenu visuel avec les API avancées de balisage d'images

उन्नत छवि टैगिंग API के साथ अपनी दृश्य सामग्री को सुव्यवस्थित करें

高度な画像タグ付け API でビジュアル コンテンツを効率化

고급 이미지 태깅 API로 시각적 콘텐츠를 간소화하세요

Simplifique seu conteúdo visual com APIs avançadas de marcação de imagens

Оптимизируйте свой визуальный контент с помощью расширенных API-интерфейсов маркировки изображений

Gelişmiş Resim Etiketleme API'leriyle Görsel İçeriğinizi Kolaylaştırın

Hợp lý hóa nội dung trực quan của bạn bằng API gắn thẻ hình ảnh nâng cao

使用高级图像标记 API 简化您的视觉内容

Authors: (1) Reza Ghaiumy Anaraky, New York University; (2) Byron Lowens; (3) Yao Li; (4) Kaileigh A. Byrne; (5) Marten Risius; (6) Xinru Page; (7) Pamela Wisniewski; (8) Masoumeh Soleimani; (9) Morteza Soltani; (10) Bart Knijnenburg. Table of Links Abstract & Introduction Background Research Framework Methods Results Discussion Limitations and Future Work Conclusion & References Appendix 5 Results 5.1 Sample Characteristics Across the older adult participants, there were 15 females and 29 males. Their average age was 68.8 years (min = 65, max = 77, sd = 3.23). The young adult sample had 87 males, 71 females, and 4 individuals who did not self-identify as female or male. Their average age was 20.30 (min = 18, max = 25, sd = 2.18). The dataset also measured participants’ Facebook usage frequency on a 9-point scale from “Never” coded as 0 to “Almost constantly” coded as 9 and the time they spend on Facebook in each session on a 5-point scale from “A few minutes” coded as 1 to “Several hours” coded as 6. On average, older adults used the Facebook platform more frequently (mean = 5.708, sd = 1.519) compared to younger adults (mean = 5.402, sd = 2.055). However, both age groups spent an average time of 30 minutes on Facebook ( older adults: mean = 2.054, sd = 1,194 and young adults: mean = 2.119, sd = 1.275). In the following, we present our analyses with regards to our hypotheses. Table 2 summarizes the hypothesis tests. 5.2 Effects on Tagging Decision In contrast to H1 and in line with the privacy paradox, we did not find any significant relationship between privacy concerns and disclosure decision (p>.05). Therefore, H1 is rejected. However, we found support for H2 and H3 suggesting both framing and default significantly influence tagging decisions. Users who see the positively framed option of “Tag me in the photos” were 31.9% more likely [2] to use the tagging feature compared to those who see the negatively framed option of “Do not tag me in the photos” (p < .001). Furthermore, those users who were being tagged by default were 19.4% more likely to use the tagging feature (p<.01). In addition, in contrast to H4, the various justifications did not influence tagging decision differently (χ2 (4) = 0.823, p > 0.05). Finally, in line with what we hypothesized in H9, older adults were 19.8% more likely to use the tagging feature compared to young adults (p<.05). 5.3 Effects on Privacy Concerns In contrast to H5 and H6, we did not find any significant effects of framing and default manipulations on privacy concerns (p > .05). However, we did find a significant main effect of justifications on privacy concerns (χ2 (4) = 9.827, p < 0.05). Subsequent planned contrast tests revealed a significant effect of justification valence (p < 0.05), supporting H7 and suggesting that negative justification messages lower users’ privacy concerns compared to positive justifications by 0.132 standard deviations. However, this effect was negated by a marginal interaction between justification type and valence, meaning that the difference between positive and negative justifications mostly hold for normative justifications (see Figure 5). Finally, in contrast to H8, older and younger adults did not have significant differences in terms of their overall level of privacy concerns (p > 0.05). Our results show that in contrast to our H8, older and younger adults have the same levels of privacy concerns. Furthermore, while having the same levels of concern, older adults are more open to disclosure. We further unpack the effects of age by studying moderation effects of age and dark pattern designs. We therefore run a saturated path model and report it below. Fig- ure 6 and Table A1 summarize our results.. 5.4 Moderating Effects of Age Group on Tagging Decision We found age group to moderate the effect of framing on tagging decision; the effect of framing was stronger for older adults than for young adults: older adults who were exposed to a positively framed option were 53.1% more likely to use the tagging feature while young adults who were exposed to a positively framed option were only 33.1% more likely to use the tagging feature (p < .05). Figure 7 depicts this effect. We found a similar moderation effect for defaults suggesting that older adults were more likely to keep the default option and use the tagging feature compared to young adults. However, this effect did not reach significance (p = .062). Similarly, age did not moderate the effect of justifications on the tagging decision (χ2 (4) = 4.822, p > 0.05). Lastly, while the effect of privacy concerns on the tagging decision was stronger for older adults than for young adults, this effect was not significant (p>.05). 5.5 Moderating Effects of Age Group on Privacy Concern The effect of defaults on privacy concerns were moderated by age group: an opt-out default increased older adults privacy concerns by 0.255 times standard deviation compared to a negative default, while an opt-out default increased young adults’ privacy concerns by only 0.066 times standard deviation compared to a negative default (p < 0.01). Figure 8 depicts this effect and suggests that an opt-out default only significantly increases the privacy concerns for older adults. Age group did not moderate the effects of any other variable on privacy concerns(p > 0.05). [2] To present the results in a comprehensive manner, we convert the log odds-ratio to percentages. For example, the effect of framing on disclosure is 0.277 (table A1), which is a log odds-ratio. Therefore, disclosure in the positive framing group is e0 .277 = 1.319 times higher than in the negative framing group, i.e., a 31.9% difference in the odds of disclosure This paper is under CC BY-NC-SA 4.0 DEED license. available on arxiv

Exploring Differences in Privacy Concerns and Tagging Behavior Among Older and Younger Adults

Picture this – you're , sitting in a futuristic spacecraft and tasked with managing a complex network of satellites. So would you rather handle it haphazardly, or do you prefer a well-labeled and meticulously organized system? thousands of miles above the earth Surely, you would choose the latter. Similarly, your and applications are that intricate network and application tagging is your to effortlessly navigate this galaxy of data. cloud infrastructure roadmap At its core, is the simple act of assigning labels – or "tags" – to your resources, offering a whole new level of clarity and control. tagging Whether you're a , enterprise decision-maker, or software engineer, your day revolves around managing and making sense of vast pools of data. With a solid tagging strategy, you enhance tech leader observability, robust decision-making, and seamless operations. From facilitating to and , good tagging is the silent champion that empowers your operations to be more agile, efficient, and effective. Let's look into the optimal strategies for effectively tagging your infrastructure and applications. maintenance optimizing costs security Understanding Tagging Source Tagging, at the basic level, is an to classify, define, and co-relate IT resources and assets within an infrastructure or an application. The purpose of tagging isn't merely regulatory, but rather much more compelling. It and improves transparency within your cloud infrastructure. And that is where the magic happens. organizational practice facilitates smarter resource management, enhances operational efficiency, Let's dive a little deeper. This is the lifeblood of effective tagging. Consistent formatting, naming conventions, and application are what make a tagging strategy truly powerful. It minimizes confusion across your teams. Consistency: Bonus point? When you're handling hundreds, or even thousands, of resources, manual tagging can be quite an arduous task. Incorporating automated tagging into your strategy is like having an invisible army at your disposal. Automation: The more detailed your tags, the better visibility you'll get into your resources. So, don't shy away from creating those detailed, granular tags. Comprehensiveness: Effective tagging isn't a one-and-done move. Revisit, refresh, and revamp - the mantra to add finesse to your tagging strategy in the long run. Review: A well-executed tagging strategy can be your knight in shining armor. It's like the ultimate roadmap to an organized, optimally functioning infrastructure. And who doesn't want that? Best Practices for Infrastructure Tagging Source Now, let's delve deeper into the nitty-gritty of organizing resources for efficient management, creating consistent, descriptive tags, and seamlessly implementing a standard tagging taxonomy. An Efficient Management of Resources Think of your cloud infrastructure as a massive library. Without organized sections and alphabetically arranged books, there is bound to be finding the information you need. The same applies here. chaos in terms of Group similar resources together. For instance, all resources related to your 'Payment Processing Solution' can be combined. This way, you're one search away from all relevant information about that particular application. Cluster wisely: Not everyone in your team needs access to everything. Restrict permissions based on roles to avoid accidental harm to important resources. Implement access controls: Creating Consistent and Descriptive Tags Each tag is a piece of metadata that adds context, making it easier to identify, organize, and search for resources. The key is to create yet tags. Here's how: descriptive consistent Your tags should be short, making them easier to remember and use. For example, instead of tagging a resource as 'PaymentProcessingProductionServer,' consider 'PP_Prod_Server.' Conciseness is key: Consistency across all tags will greatly pay off in the long run. Stick to a specific format so that any team member can understand the tag without much confusion. Stick to a naming convention: Implementing a Standardized Tagging Taxonomy Having a standardized tagging taxonomy means having a set tagging strategy that everyone sticks to. Let's illustrate with a simple example. One team in your organization may prefer the tag 'env=prod' while another may use 'environment=production' for the same thing. This inconsistency not only leads to confusion but also potential losses in resource management. Keep an up-to-date record of all tags and their descriptions for everyone to refer to. Maintain Documentation: Automation reduces human error. Using automated tagging solutions can allow for better accuracy and consistency. Automate the process: In conclusion, with these strategies, you're not just tagging your infrastructure and applications, you're adding a layer of efficiency and productivity that will greatly boost your overall operational success! Application Tagging Techniques Source Do you know what's great? Harnessing the right from the inception of any application. Can you guess why? It's because Yes, you heard that right! power of tagging tagging aligns perfectly with the different stages of an application's lifecycle! Aligning Tags with Application Lifecycle Stages Imagine for a moment that your application is in the development stage. Having , will make information retrieval a breeze during this phase. But, what happens when your application moves to the test stage? Simply tweak those tags from to ! It's like having a virtual assistant guiding your operations team throughout the application lifecycle. tags specific to "Development" "Development" "Testing" Let's have a look! "But how can tagging help in monitoring and troubleshooting?" Leveraging Tags for Enhanced Monitoring and Troubleshooting What's your first response when your application encounters an error? You investigate it, right? Now, imagine if your applications are neatly tagged per their roles, environment, and ownership. saves you from diving into the sea of code or sifting through log data, thus accelerating the troubleshooting process by a huge margin! Now that's what is called efficient monitoring! A simple filter on these tags Apply the tag "FrontEnd" to your application dealing with user-facing roles. When an error occurs, filter by this tag, and you'll instantly focus on resolving bugs that impact user experience! Example: Incorporating Security Measures Through Tagging Whoever thought that tagging could be your silent crusader in securing application infrastructure, right? As astonishing as it may sound, it's true! You can enforce security policies based on tags, thereby enhancing your application's overall security posture. For example, consider having a tag that says "PCI-DSS Compliant." Applying this tag to all relevant applications not only helps in but can also flag any application that isn't compliant! How's that for a guard dog protecting your application on the cloud? streamlining compliances So, isn't it interesting how effective tagging can streamline application lifecycle management, enhance monitoring and troubleshooting, and play a critical role in application security? Go ahead, and start tagging! Automation in Tagging Let's be honest here. As technology leaders and DevOps engineers, keeping track of the countless assets within our businesses may be just as daunting as herding cats. Believe it or not, we're all sailing in the same boat of massive infrastructures and applications. But have you ever wondered if this can be made any easier, smoother, or even automated? Well, here's the solution: automated tagging and streamlining processes that clinch the accuracy, consistency, and efficiency we all seek. Need some kind of for your tagging conundrums? Let's walk you through the game-changers. 'autopilot' With the ability to automatically tag infrastructure elements and applications, these tools can be monumental in minimizing manual intervention. They are, quite literally, lifesavers in AWS, Azure, and more. Some leading solutions include Middleware, AWS Auto Tagging, and Azure Policy. You won't believe the hours of toil they’re saving worldwide. Automated Tagging Tools: These have practically redefined automation. By providing comprehensive orchestration across different cloud environments, CMPs such as CloudCheckr and Flexera make tagging a breeze. They ensure you're not just tagging, but tagging right—with accuracy and consistency. Cloud Management Platforms (CMPs): Needless to say, any automated tagging discourse would be incomplete without acknowledging the role of DevOps tools such as Chef, Puppet, Ansible, and Kubernetes. They have rewritten the rules, enabling automated environment set-up, application deployment, scaling, and yes, even tagging. DevOps Tools: Now, these tools are not magic wands, and you surely can't just go . But if implemented correctly, they can offer , possibly saving you hours and a lot of stress. set it and forget it remarkable reliability and replicability in your tagging practices Case Studies Let's explore some where infrastructure and application tagging have been successfully implemented, and dive into the lessons learned and key takeaways. case studies Case Study 1 - Atlassian - Automating Infrastructure and Application Discovery with CloudSploit There was a time when the discovery of resources was a tedious, error-filled task. The limited insight into how cloud assets interacted elevated the risk of security vulnerabilities and non-compliance with regulations. Challenge: They integrated CloudSploit, which allows for seamless infrastructure and application discovery. It automatically assigns tags to your resources, providing you with crucial data and all the associated application dependencies. Strategy: Experienced a significant decrease in the time devoted to discovery and tagging and bolstered the security stance by promptly identifying and remedying any vulnerabilities. Outcome: Proper tagging, coupled with diligent oversight and automation, can lead to massive cost savings. Key Takeaway: Case Study 2 - Spotify - Streamlining Cloud Management with Tagging in OpenTelemetry Difficulty managing complex microservice architecture with diverse deployment environments. The lack of standardization and consistency in tagging made resource identification difficult. Challenge: Adopted OpenTelemetry, an open-source framework for distributed tracing and monitoring, and implemented a standardized tagging strategy across applications and infrastructure using labels. Strategy: Simplified cloud resource management with consistent and searchable tags. Achieved better observability and troubleshooting through granular tracing data. Outcome: Strategic application tagging can both elevate application performance and fortify security. Key Takeaway: Case Study 3 - Netflix - Scaling Cloud Operations with Tagging in CloudFormation and AWS Cost Tagging Difficulty managing large-scale infrastructure across multiple regions and accounts. The lack of granular costing insights hindered cost optimization efforts. Challenge: Utilized AWS CloudFormation for infrastructure provisioning and tagged resources with application names, environments, and ownership details. Enabled AWS Cost Tagging to track costs by application and team, facilitating granular cost allocation and optimization. Strategy: Improved operational efficiency by automating infrastructure provisioning and tagging. Gained detailed cost insights enabling informed resource allocation and budgeting decisions. Outcome: Effective infrastructure tagging can simplify compliance and create a more efficient audit system. Key Takeaway: These success stories underscore the . When properly implemented and managed, tagging starts to look less like a chore and more like a critical strategy for business success. transformative potential of effective tagging Future Trends in Tagging Practices Let's not beat around the bush here, folks. The breathless pace of technology's evolution has rippled through the world of tagging like a virtual tsunami. Take a minute to consider this. A staggering have adopted some form of cloud technology according to a . The explosion of cloud-native applications has shifted the focus to more effective and precise tagging. As a result, the old tag-and-hope methods just don't cut it anymore. 92% of organizations Faction report The Changing Winds of Tagging With the technological horizon constantly mutating, the ways we handle tagging our infrastructure and applications need to shift gears too. Consider these points: Emerging technologies like AI and ML are making a splash in the tagging world. AI/ML-powered automation can greatly reduce human errors in tagging, making your applications and infrastructure run more smoothly. Artificial Intelligence(AI) and Machine Learning(ML): According to a , 94% of tech leaders emphasize the shift towards observability. This means adopting tagging strategies that highlight and communicate the right information, swiftly and accurately. Shift towards Observability: NewRelic 2020 report We all know that change can be challenging, especially in the volatile realm of technology. But when it comes to preparing for the future of infrastructure and application tagging, it's time to the change. Why merely adapt? Let's evolve, together. embrace Conclusion We can't stress enough how significant it is to have a good grip on your and what role effective tagging plays. Remember, - it's a performance game-changer that can fuel agility, efficiency, and scalability. Making in your IT team isn't just about diligent practice but fostering a better understanding and communication. IT operations tagging is not just a 'nice to have' tagging a culture Let's do a quick recap of best practices for tagging: Uniformity in your tagging approach ensures seamless identification of resources. Be Consistent: Tags should provide clear insights about the resource at a glance. Stay Descriptive: Crafting a classification system expedites resource management. Adopt a Standardized Taxonomy: Tagging is no longer an option; it is an essential tool in the modern IT Toolkit.

Optimal Strategies for Effectively Tagging Your Infrastructure and Applications

As much as we say is an all-in-one data platform that is capable of various analytics workloads, it is always compelling to demonstrate that by real use cases. That's why I would like to share this user story with you. It is about how they leverage the capabilities of Apache Doris in reporting, customer tagging, and data lake analytics and achieve high performance. Apache Doris This fintech service provider is a long-term user of Apache Doris. They have almost 10 clusters for production, hundreds of Doris backend nodes, and thousands of CPU Cores. The total data size is near 1 PB. Every day, they have hundreds of workflows running simultaneously, receive almost 10 billion new data records, and respond to millions of data queries. Before migrating to Apache Doris, they used ClickHouse, MySQL, and Elasticsearch. Then, frictions arise from their ever-enlarging data size. They found it hard to scale out the ClickHouse clusters because there were too many dependencies. As for MySQL, they had to switch between various MySQL instances because one MySQL instance had its limits, and cross-instance queries were not supported. Reporting From ClickHouse + MySQL to Apache Doris Data reporting is one of the major services they provide to their customers and they are bound by an SLA. They used to support such service with a combination of ClickHouse and MySQL, but they found significant fluctuations in their data synchronization duration, making it hard for them to meet the service levels outlined in their SLA. Diagnosis showed that it was because the multiple components add to the complexity and instability of data synchronization tasks. To fix that, they have used Apache Doris as a unified analytic engine to support data reporting. Performance improvements With Apache Doris, they ingest data via the method and reach an SLA compliance rate of over 99% in terms of data synchronization performance. Broker Load As for data queries, the Doris-based architecture maintains an of less than and a of less than . This is a 50% speedup compared to the old architecture. average query response time 10s P90 response time 30s Tagging Tagging is a common operation in customer analytics. You assign labels to customers based on their behaviors and characteristics so that you can divide them into groups and figure out targeted marketing strategies for each group of them. In the old processing architecture, where Elasticsearch was the processing engine, raw data was ingested and tagged properly. Then, it will be merged into JSON files and imported into Elasticsearch, which provides data services for analysts and marketers. In this process, the merging step was to reduce updates and relieve load for Elasticsearch, but it turned out to be a troublemaker: Any problematic data in any of the tags could spoil the entire merging operation and thus interrupt the data services. The merging operation was implemented based on Spark and MapReduce and took up to 4 hours. Such a long time frame could encroach on marketing opportunities and lead to unseen losses. Then Apache Doris takes this over. Apache Doris arranges tag data with its data models, which process data fast and smoothly. The aforementioned merging step can be done by the , which aggregates tag data based on the specified Aggregate Key upon data ingestion. The is handy for partial column updates. Again, all you need is to specify the Unique Key. This enables swift and flexible data updating and saves you from the trouble of replacing the entire flat table. You can also put your detailed data into a to speed up certain queries. . Aggregate Key model Unique Key model Duplicate model In practice, it took the user 1 hour to finish the data ingestion, compared to 4 hours with the old architecture In terms of query performance, Doris is equipped with well-developed bitmap indexes and techniques tailored to high-concurrency queries, so in this case, it can finish and reach over . customer segmentation within seconds 700 QPS in user-facing queries Data lake analytics In data lake scenarios, the data size you need to handle tends to be huge, but the data processing volume in each query tends to vary. To ensure fast data ingestion and high query performance of huge data sets, you need more resources. On the other hand, during non-peak time, you want to scale down your cluster for more efficient resource management. How do you handle this dilemma? Apache Doris has a few features that are designed for data lake analytics, including Multi-Catalog and Compute Node. The former shields you from the headache of data ingestion in data lake analytics while the latter enables elastic cluster scaling. The mechanism allows you to connect Doris to a variety of external data sources so you can use Doris as a unified query gateway without worrying about bulky data ingestion into Doris. Multi-Catalog The of Apache Doris is a backend role that is designed for remote federated query workloads, like those in data lake analytics. Normal Doris backend nodes are responsible for both SQL query execution and data management, while the Compute Nodes in Doris, as the name implies, only perform computation. Compute Nodes are stateless, making them elastic enough for cluster scaling. Compute Node The user introduces Compute Nodes into their cluster and deploys them with other components in a hybrid configuration. As a result, the cluster automatically scales down during the night, when there are fewer query requests, and scales out during the daytime to handle the massive query workload. This is more resource-efficient. For easier deployment, they have also optimized their Deploy on Yarn process via Skein. As is shown below, they define the number of Compute nodes and the required resources in the YAML file, and then pack the installation file, configuration file, and startup script into the distributed file system. In this way, they can start or stop the entire cluster of over 100 nodes within minutes using one simple line of code. Conclusion For data reporting and customer tagging, Apache Doris smoothens data ingestion and merging steps and delivers high query performance based on its own design and functionality. For data lake analytics, the user improves resource efficiency by elastic scaling of clusters using the Compute Node. Along their journey with Apache Doris, they have also developed a data ingestion task prioritizing mechanism and contributed it to the Doris project. A gesture to facilitate their use case ends up benefiting the whole . This is a great example of open-source products thriving on user involvement. open source community Also published . here

Data Analytics: Apache Doris' Impact in Reporting, Tagging, and Data Lake Operations

Music is a universal language that brings joy and rhythm to our lives. With the rise of digital music in the form of MP3 files, managing and organizing our music libraries can become overwhelming. But fear not, fellow music enthusiasts! MP3 tagging is here to save the day and bring order to the chaos. In this article, we'll dive into the world of MP3 tagging, uncover its importance, explore common , learn how to tag MP3 files manually or with automated tools, discover the benefits of proper MP3 tagging, troubleshoot tagging issues, and wrap it up with some fun FAQs to keep you grooving. Let's get tagging! MP3 tags What is MP3 Tagging? MP3 tagging is the superpower that allows you to add metadata or information to your files. Think of it like dressing up your tracks with tags that contain details such as song titles, artist names, album names, genres, track numbers, and even album covers. These tags are stored within the MP3 file itself and can be read by music players or media management software to display valuable information about the music track. It's like giving your music files a makeover and making them shine! MP3 music Importance of MP3 Tagging Tagging your MP3 files may seem like a small task, but it carries significant importance for music lovers and collectors alike. Properly tagged MP3 files offer several benefits, including: With accurate and consistent MP3 tags, you can quickly search, sort, and filter your music collection based on criteria such as artist name, album name, or genre. No more endless scrolling or searching for that one song buried deep in your library! Efficient Music Library Management: MP3 tags provide valuable information about the song, album, and artist, which is displayed by music players. You can easily identify and select the right track, and even view album covers or lyrics, enhancing your overall listening experience. Enhanced Music Listening Experience: MP3 tags help you maintain consistency in the naming conventions of your music files. No more misspelled or inconsistent song titles, artist names, or album names. It's like having your own music catalog with uniform and organized information. Standardized Naming Conventions: Common MP3 Tags MP3 tags can vary depending on the music player or software used, but here are some common tags you'll come across: Title: The name of the song Artist: The name of the artist or band Album: The name of the album Genre: The genre of the music Year: The year of release Track Number: The track number of the song in the album Album Cover: The album artwork or cover image Lyrics: The lyrics of the song Composer: The name of the composer Comments: Additional comments or notes about the song How to Tag MP3 Files Now that we understand the importance of MP3 tagging, let's explore how to files effectively. There are two main methods: manual tagging and automated tools. tag MP3 Manual Tagging Methods Manual tagging involves editing MP3 tags one by one using a music player or media management software. Here's how you can manually tag your MP3 files: Open your preferred music player or media management software. Locate the MP3 file you want to tag and select it. Right-click on the file and choose the "Properties" or "Get Info" option. In the properties window, go to the "Details" or In the properties window, go to the "Details" or "Info" tab. You'll see fields for different MP3 tags such as Title, Artist, Album, etc. Fill in the relevant information for each tag field based on the details of the MP3 file. Click on the "OK" or "Apply" button to save the changes. Automated Tagging Tools If you have a large music library or prefer a more automated approach, you can use MP3 tagging software or online tools. These tools can automatically scan and tag MP3 files based on their audio fingerprints or online databases. Here are some popular automated tagging tools: : This free and open-source software is known for its accurate audio fingerprinting and vast music database that helps in tagging MP3 files automatically. MusicBrainz Picard Mp3tag: This popular software allows you to edit and batch tag MP3 files using various sources such as online databases, file names, and folder names. TuneUp: This commercial software offers an easy-to-use interface and uses acoustic fingerprinting to automatically tag MP3 files. Benefits of Proper MP3 Tagging Properly tagged MP3 files can revolutionize your music library management and listening experience. Here are some benefits of investing time and effort into proper MP3 tagging: Easy Music Organization: With accurate and consistent MP3 tags, you can easily search, sort, and filter your music collection based on different criteria, making music organization a breeze. Enhanced Music Playback: Properly tagged MP3 files provide valuable information that is displayed by music players, making it easy to identify and select the right track, view album covers or lyrics, and enhance your overall music playback experience. Uniform Naming Conventions: MP3 tags help you maintain uniform and organized naming conventions for your music files, avoiding misspelled or inconsistent song titles, artist names, or album names. Improved Music Discoverability: Properly tagged MP3 files make it easier to discover new music based on genre, artist, or album, expanding your music horizon and helping you find your favorite tracks more quickly. Troubleshooting MP3 Tagging Issues As with any technology, MP3 tagging may encounter some issues. Here are some common troubleshooting tips for MP3 tagging problems: Different music players or software may use different formats for MP3 tags, leading to inconsistencies. Make sure to use consistent tag formats or use a tagging tool that can automatically standardize your MP3 tags. Inconsistent Tag Formats: Sometimes, online databases or audio fingerprints may not provide accurate information for MP3 tagging. In such cases, manually verify and correct the information to ensure accurate tagging. Missing or Incorrect Information: Some music players or software may not support certain MP3 tags, leading to issues with displaying or accessing the tagged information. Check the compatibility of MP3 tags with your music player or software to avoid such problems. Unsupported Tags: MP3 files may get corrupted, leading to issues with tagging. Make sure to use reliable sources for your MP3 files and consider using file repair tools if you encounter tagging issues with specific files. Corrupted MP3 Files: FAQs (Frequently Asked Questions) Q: Can I tag MP3 files on my mobile device? A: Yes, there are several mobile apps available that allow you to tag MP3 files on your mobile device, making it convenient for music management on the go. Q: Can I edit MP3 tags after I've already tagged them? A: Yes, you can always edit MP3 tags even after you've already tagged them. Simply locate the MP The lead image for this article was generated by HackerNoon's AI Image Generator via the prompt "music studio".

MP3 Tagging: Unleash the Power of Tagging and Groove to Organized Music Madness

Etiquetado de MP3: Libere el poder del etiquetado y disfrute de la locura musical organizada

Balisage MP3 : Libérez le pouvoir du balisage et du groove dans la folie musicale organisée

एमपी3 टैगिंग: संगठित संगीत पागलपन के लिए टैगिंग और ग्रूव की शक्ति को उजागर करें

MP3 タグ付け: タグ付けとグルーヴの力を解き放ち、組織化された音楽の狂気へ

Marcação de MP3: libere o poder da marcação e divirta-se com a loucura da música organizada

Gắn thẻ MP3: Giải phóng sức mạnh của việc gắn thẻ và theo dõi sự điên rồ của âm nhạc có tổ chức

MP3 标记：释放标记和 Groove 的力量来组织疯狂的音乐

A shopper lands on your website and navigates to the search bar. She wants a red skirt, mid-length, with a fun, playful print. When she types in her search query for “red midi skirt,” the shopper receives dozens of relevant search results. The building blocks of these search results (and even product recommendations) is image tagging (also known as fashion tagging). Behind the scenes, a human tagged each of the product attributes by hand, a painful task when considering the hundreds or thousands of products uploaded on an eCommerce website per day. Unfortunately, manual image tagging can take hours to complete and is prone to errors. For example, one human tagger might label the red skirt as “mid length skirt” and another human tagger might consider the skirt to be a “midi-skirt,” leading to inaccurate product data. But, is there an easier way to tag that saves time, energy, and is more accurate? The answer: building an artificial intelligence that understands fashion product attributes to correctly tag information. In this blog post, we’ll walk you through: Image tagging 101 What is good image tagging? How YesPlz image tagging works The impact of good fashion tagging Image Tagging 101 Image tagging is the process of labeling product attributes on an image. A product attribute can include all different elements of a product, from a design style to color and material type. A single clothing category can have between 20 to 60 different attributes, which can take significant time to tag the attributes of a single product image. If we look at the dress below, it would have about 9 different attributes including dress category, neck design, material, and mood or occasion. Traditionally, a member of the merchandising/eCommerce team is responsible for tagging. Designers and third-party vendors provide tagging information, and the in-house team is responsible for cleaning up and organizing the tags. But, when information comes in from third parties, it can be inconsistent. Depending on the vendor, they may have a different word for "lace"--and when you take into account the sheer volume of products that need to be tagged, this inconsistency creates a mess for fashion eCommerce. Alternatively, the in-house merchandising experts may make tagging mistakes (we're all human, after all). Because of the complications of fashion tagging, some fashion eCommerce brands may skip it altogether to save time. While it may be tempting to skip, without your eCommerce brand can't: accurate image tagging, provide a superior search experience to shoppers categorize products create product descriptions improve search results offer more filtering options generate recommendations utilize powerful personalization tools Image tagging contains crucial information that enriches a shopper’s online experience. If your eCommerce is looking to implement the next great personalization tool, image tags provide the building blocks to do so. Image tagging creates the foundation for a superior shopping experience When asked to describe a superior online shopping experience, shoppers will consistently answer that search and recommendations are key differentiators in the crowded fashion eCommerce space. Without fashion tagging, shoppers need to rely on very limited information to find what they’re looking for–and they have difficulty finding search results that meet their needs. Bad image tagging creates a bad online shopping experience. While shoppers may not understand the mechanics of why their experience is bad, they will interpret a poor online shopping experience as a cue to not return. Image tagging doesn’t need to be a “necessary evil” for retailers–but can be an enjoyable experience. For retailers, fashion tagging is a necessary but tedious task. When manually tagging, a single batch of products requires days of work–including tagging and double-checking fashion terminology. And we know that retailers are tired of doing work–and receiving little in return. Unfortunately, it’s not an option to skip image tagging–without it, retailer product catalogs would be disorganized. Without fashion tagging, you can’t have powerful and accurate site search, fashion-forward recommendations, or well-organized data to personalize user experiences. But, poor automated tagging tools can still cost retailers hours of time, correcting mistakes and re-sorting taxonomies. What makes a good image tagging solution? There are hundreds of image tagging tools on the market, but not all of them are created equal. Even if they’re powered by artificial intelligence, it’s important to remember that artificial intelligence is only as good as the input data that trained the algorithm. And, when artificial intelligence makes mistakes, fashion experts with a deep understanding of fashion should be the ones to review those mistakes. Other considerations when choosing a fashion tagging tool are the quality of the tags (are they accurate?) and speed (slow tagging means more work for retailers). Lastly, we recommend that you review the types of tags you’d like to see (specific styles, details, moods, or occasions) and make sure that the image tagging tool can quickly and accurately tag for your criteria. Image Tagging In Action: Searching For A Shirtdress A shopper wants to buy a shirtdress, defined as a women's dress with a collar and buttons. She types in her search query and receives two sets of results: Result #1: Search Results Without Image Tagging The retailer relied on text tagging to create product tags, leading to mixed search results that include t-shirt dresses and men’s shirts–unrelated to the shopper’s original search query. These search results highlight the importance of image tagging–a good image tagging tool would be able to identify the product type and product attributes. Result #2: Search Results With Image Tagging Next, the shopper searches for a shirtdress with YesPlz’s Style Filter. She receives the following search results, which are clear and accurate: The difference between the two search results? Image tagging. In the second set of search results, YesPlz AI image tagging has correctly identified and tagged the product attributes, leading to accurate, robust search results. Are you curious to know more about how works? Let’s go behind the scenes and explore the YesPlz process and why we’re different from other solutions. YesPlz image tagging How Our Image Tagging Process Works Here is the YesPlz process for ensuring accurate and fast image tagging: Step 1: Before building our tool, we defined key fashion attributes that matter to shoppers through user interviews. We started with user interviews and sat down with shoppers to define what key fashion attributes they care about the most. Our interviews took hours of probing each individual to find out more about their most important attributes. And, as a result, we’re happy to say that our image tagging tool reflects the real needs of shoppers. Because of the preparation we’ve put into our image tagging tool, retailers can be assured that our fashion tagging tool tags product attributes that shoppers are searching for, such as fit, style, and occasion. We have a clear understanding of the criteria for image tagging, leading to more accurate tagging from the start. Step 2: We train the AI using image and text–then fashion experts correct the AI. Through a combination of computer vision and Natural Language Process (NLP) technology, YesPlz AI is able to do deep image tagging which is accurate, fast, and seamless. AI Training: In addition, our technology can identify product attributes even in all sorts of image types whether the clothing image is on or off a model, even if the image has a noisy background or is user generated content. Step 3: Fashion expert intervention: AI will inevitably make incorrect inferences–but who is catching those mistakes? At YesPlz, our fashion data annotators are from Parsons School of Design and Fashion Institute of Technology. They understand the details of fashion, and are up-to-date on the latest fashion trends and terminology. We have fashion data annotators in-house that speak the language of fashion, leading to highly accurate tagging. The result of AI training with computer vision and NLP, combined with our data annotators from fashion backgrounds is fast, accurate image tagging for retailers. Step 4: We provide data enrichment and image tagging output for retailers. The last step in the YesPlz image tagging process is output which helps retailers make better decisions through data. We provide retailers with data enrichment using an API that allows retailers to easily receive image tagging information in an easy-to-use format that fits their preferences. In addition, our data output is available in English, Korean, and Arabic. The impact of good fashion tagging Good, quality fashion tagging is necessary to build out dynamic search and recommendations and hyper-personalization. While image tagging is very much “behind the scenes,” your customers are affected by poor image tagging when they receive mismatched search results or off-target recommendations. Image tagging, when accurate and quick, can provide better quality data for your fashion eCommerce, which you can then use for personalization. But, the common objections we’ve seen to image tagging are: it’s inaccurate it takes too long YesPlz AI is solving these problems for retailers by providing fast, accurate tagging. Our AI is trained to recognize product attributes in all types of pictures, even low-quality images, and is then reinforced with the input of fashion experts. Are you interested in keeping up-to-date with the latest retail and eCommerce trends? Follow YesPlz AI on LinkedIn for updates. This story was published by next-gen fashion search. YesPlz AI,

What Makes "Good" Fashion Image Tagging

After years of development, we decided to standardize the user interface of one of our most important products – our multi-application dashboard. We did this for our customers and internal users (ease of use) as well as our product team (easier design process, decision-making, and coding). We also needed to align more consistently with our company’s branding. For this, we built an , called Satellite. internal design system In developing Satellite, we looked at different CSS solutions for the UI library, all with their pros and cons: , , . Saas css modules css-in-js After considering frameworks similar to Bootstrap, we settled on the CSS framework . Why? Tailwind CSS Pure CSS (no JS runtime) – good for performance. Tends to generate smaller CSS stylesheet files (after purging) – also good for performance. No switching between a CSS file and your javascript code while developing new components. No time wasted trying to find good names for utility classes. Helps promote UI consistency. Allows you to define a collection of spacings and colors that map well to design tokens (a “Restricted Palette”). However… there was a downside to Tailwind: The soup of Tailwind can be hard to digest when you’re not used to its classnames. the readability of complex components. In our case, it was made worse because we had to use a prefixed version of the CSS classes ( ) to avoid conflicts with our legacy CSS, adding even more noise and length to our classname strings. stl- This article shows how we mitigated the readability problem. For starters, we used several web development techniques, such as tagged literals and interpolation, to shorten the length of our strings. Then we simplified the usage of the classnames with the linter tool ESLint, providing a better DX with two tools: An ESLint plugin, because there was no at the time. official ESLint-Tailwind plugin A Visual Studio Code extension to simplify the usage by providing intellisense of Tailwind’s many classes. The didn’t work for us because it expected a configuration file ( ) to be present in the project, while we had used a prebuilt version at the time. Among other tasks, we needed VS to work with our config file. official ESLint VS extension tailwind.config.js That’s more or less the background. Now let’s get into the implementation. Tailwind – Classnames are Good, but Can Get Complex Classnames are Good A utility-first CSS framework like comes with a large set of pre-existing utility classes that you can use directly in your HTML and JavaScript. These classes enable consistency across the code. Tailwind And they are entirely configurable: with the same classnames, we could easily brand our application with variants. Therefore, using Tailwind CSS classnames enabled us to create a consistent set of colors, spacing, fonts – essentially, all things CSS – and rolled out an easy-to-implement design system. But Tailwind Classes Can Get Complex We wanted to simplify our usage of Tailwind’s classes. For this, we used techniques such as tagged template literals, interpolation, and conditions. We started with a long string of CSS classes like the following: const className = 'stl-inline-flex stl-items-center stl-justify-center stl-rounded-full stl-h-10 stl-w-10 stl-bg-blue-100'; But we soon realized that this was not easy to read. Additionally, it contained unnecessary noise, such as the prefix , used to avoid clashes with other classes. stl- So we enlisted the help of to remove the prefix from the string. We created an tagged template literals stl tag: const className = stl 'inline-flex items-center justify-center rounded-full h-10 w-10 bq-blue-100'; Finally, we wanted more readability. So we added: Separate lines for more readability and grouping of common elements Interpolation with inline tagged template literals Conditions for more powerful and adaptive styling The result is an elegant piece of (CSS) code: const className = stl '
inline-flex items-center justify-center
h-10 w-10
${round && 'rounded-full'}
${iscool ? 'bg-blue-100' : 'bq-red-100'}
; ESLint – Effortlessly Correcting Human Error Elegance is one thing. Correct is another. It’s easy to misspell classes, especially when there are many classes to initially learn about in Tailwind. Here’s an example of what can go wrong: cost className = stl 'felx space-between text-gray-200’; Can you spot the errors? Switching letters (felx for flex) Incomplete or non-existent classes (space-between) American vs British spelling (gray) ESLint to the Rescue – Creating an ESLint Plugin We needed to verify that the classes that people used were the correct ones. So we used the linter tool to parse the code, analyze it, and report errors. ESLint We created an ESLint plugin for code quality and to report errors on class names that didn’t exist. Here’s the central ESLint code that does the validation: ESLint uses an abstract syntax tree (AST) that gives access to individual lines of code. The AST essentially converts the strings of the code into nodes, which you can parse as collections and elements. Here’s the breakdown of how ESLint parses the code. The whole expression is a of type : node VariableDeclataion We want to parse the expression on the right side, the . TaggedTemplateExpression As you can see, there’s a callback that handles this kind of expression: In the callback, we collect all the strings in that template. For example: TaggedTemplateExpression The TemplateElement The Literals Once the collecting is done, there’s another registered callback that loops through the collected class names and confirms whether they exist. It does this with the collection, : validClassNames That’s it. We knew right away that creating this validation plugin was the right thing to do, because we actually found a few misspellings in our system, as well as in the existing dashboard codebase! Proposing Suggestions With Our ESLint VisualStudio Extension The last tool we created was an extension in Visual Studio Code. Using the same logic as in our plugin, ESLint suggests typescript classes as a developer types. This intellisense keeps the developers from guessing or having to go to the Tailwind website to search for and find the correct classes. As you can see in the GIF, it not only proposes class names, it also displays the colors or useful icons for each suggestion. With Tailwind CSS and ESLint, we’ve been able to enforce our standards (accessible internally on Github) by improving the DX in implementing them.

How to Simplify Tailwind CSS Using ESLint, Tagged Template Literals, and More!

¡Cómo simplificar Tailwind CSS usando ESLint, literales de plantillas etiquetadas y más!

Comment simplifier le CSS Tailwind à l'aide d'ESLint, de littéraux de modèles balisés, et plus encore !

ESLint, टैग किए गए टेम्प्लेट लिटरल्स, और बहुत कुछ का उपयोग करके टेलविंड सीएसएस को सरल कैसे करें!

ESLint、タグ付きテンプレート リテラルなどを使用して Tailwind CSS を簡素化する方法

Como simplificar Tailwind CSS usando ESLint, Tagged Template Literals e muito mais!

Cách đơn giản hóa CSS Tailwind bằng ESLint, Tagged Template Literals, và hơn thế nữa!

如何使用 ESLint、标记模板文字等简化 Tailwind CSS！

In this blog, guest writer Feifei Cai (Senior Software Engineer), Hao Zhu (Senior Software Engineer), shares the practice of using Alluxio and Spark to accelerate the auto data tagging system in WeRide, an autonomous driving technology company. The original content was published on Alluxio's Blog (Disclaimer: The author is a Founding Member @Alluxio). About WeRide is a global leading company that develops L4 autonomous driving technologies. Established in 2017, WeRide is headquartered in Guangzhou, China, and maintains R&D and operation centers in Beijing, Shanghai, Nanjing, Zhengzhou, Shenzhen, as well as San Jose in the USA. WeRide WeRide launched China’s first commercial Robotaxi service, completely open to the public in 2019. Now WeRide offers an all-rounded product mix on the Robottaxi, Mini Robotbus, and Robotvan. We provide multiple services including online ride-hailing, on-demand transport, and urban logistics. Why Alluxio There are three major benefits Alluxio provides – it’s fast, simple, and easy to use. First, Alluxio is fast as it provides distributed caching. Memory speed I/O is provided by Alluxio's caching service. In addition, the tiered storage can leverage both memory and disk, making it elastic to scale data-driven applications in a cost-effective way. Second, Alluxio provides simplified and unified data access and data management. Finally, it's very easy to deploy and use Alluxio in the existing data platform. For example, Spark can run on top of Alluxio without any code changes, just one line of configuration change. How Are We Using Alluxio at WeRide Autonomous Driving Development The diagram above shows a typical workflow of autonomous driving development in WeRide. We start with data collection, collecting sensor data from the test vehicle fleets every day. Once the data is collected, we upload it to the cloud. The data is then processed and analyzed. Afterward, we will do data labeling, HD map labeling, and development. Next, we will perform machine learning model training for AI algorithm development. Simulation and validation will also be conducted. Finally, we will deploy both on-boarding vehicle fleets and off-boarding servers. We have both hardware and software in the loop. The biggest challenge is the size of the data. We generate many terabytes of data every day and have accumulated millions of kilometers of mileage, so the entire size is in petabytes. This requires vast amounts of storage and significant computing power. Data Tagging Tags are sometimes confused with labels. In autonomous driving, tags usually mean scenarios, while labels can be obstacles or boxes. For example, the left image below shows a lane-change scenario and the right image is a right-turn scenario. We will tag a sequence of frames with lane-change or right-turn tags. The labels here are the lane, the surrounding vehicles, obstacles, and the traffic lights, which are different from tags. In a data-driven autonomous driving workflow, data tagging plays an essential role in data analytics. With the tagging results, we can do data selection to support the downstream pipelines, including model training, benchmark, and long-tail evaluation. Alluxio + Spark in Kubernetes An end-to-end tagging pipeline is to collect and upload the ROS (Robot Operating System) bag files from the test vehicles and then convert them into Parquet format. After that, we can run Spark jobs to perform data analytics algorithms. From there, we can export the tagging results to downstream pipelines. As can be seen above, the system architecture is quite typical. We build and run applications on hybrid cloud with both public cloud and on-prem datacenters with the following considerations: the on-prem datacenter is more cost-effective and can be customized, while public cloud is easy to scale. In both public cloud and on-prem data centers, we use Kubernetes to manage the computing resources of the CPU and the GPU clusters and to schedule the workflow and tasks. In the storage layer, we use MinIO to build S3 compatible object storage service. Alluxio provides a fast, simplified, unified data layer that bridges S3, MinIO and Spark, and other data analytics applications so that the data tagging workflow is accelerated with the introduction of Alluxio. Test Results We followed for the deployment. We used the helm chart to deploy a test cluster across the CPU and GPU clusters for 42 workloads. We used tiered storage - level zero is the memory with 50G, and level one is the SSD with 200G. Based on 42 worker nodes, we ended up with 2TB of memory and 8TB of SSD. Alluxio documentation For the data tagging tests, there's only one line of a configuration change, which is very easy to use. We have added Alluxio into the command-line tools, but Alluxio is enabled by default and hidden from the end-users. Overall, the test shows approximately . 7x faster in performing tagging tasks with the introduction of Alluxio Future Work Use Alluxio as a Unified Data Orchestration Layer We plan to build the unified data layer with Alluxio as the data orchestration layer. Our goal is to integrate with more applications, including Presto, HIVE, and TensorFlow, which are the services already running inside WeRide. Furthur Accelerate the Workflow with Spark + GPU + Alluxio We are integrating Spark, RAPIDS, and GPU with Alluxio to get more acceleration, not just for auto data tagging but also in many other applications. (Disclaimer: The author is a Founding Member @Alluxio)

How to Accelerate Auto Data Tagging for Autonomous Driving in Hybrid Cloud

Before ES6(ECMAScript 2015), we have used single quotes('...') and double quotes("...") to wrap string literals. A simple example is, msg = ; var "Hello, I'm Joe and my favorite color is purple" There were limitations when we had to concatenate multiple strings and the string literal has dynamic values. The readability of these concatenations used to be a challenge as well. frag1 = ; val1= ; frag2 = ; val2 = ; msg = frag1 + + val1 + + frag2 + + val2; var "Hello, I'm" var "Joe" var "and my favorite color is" var "purple" var ' ' ' ' ' ' Do you see another problem? If someone only reading the line where the concatenation takes place, he/she doesn't have much idea of the resultant string. With ES6 we have got template literals which are string literals that allow embedding expressions. Template literals are enclosed by the backtick (` `) characters instead of single or double-quotes. Template literals can contain placeholders that are specified by the dollar sign($) and curly braces(${expression}). The above example can be written with template literals as, name = ; color = ; message = ; .log(message); const 'Joe' const 'purple' const `Hello, I'm and my favorite color is ` ${name} ${color} console Output, Hello, I'm Joe and my favorite color is purple This is much better and favorable for developers to use. What is the Tagged Template Literal? A Tagged Template Literal is usually a function that precedes a template literal to help you in manipulating the output. It's fine if you find it confusing. We will understand it in a few simple steps. Let us take an example of this template literal again, `Hello, I'm and my favorite color is ` ${name} ${color} We want to manipulate the output such that, it returns a string like the below when we pass the name as, Joe and color as, green. Hello Joe, Have a Nice Day! We know your favorite color is green How about, displaying this message in the color that is passed as an expression to the template literal? Like this when the color value is green, Welcome Tag function Let us first create a tag function. This is a regular JavaScript function that should return a value as per your needs. This return value is usually a manipulated output based on the template literal strings and expressions. { ;
} ( ) function introduce return 'introduce...' Next, We mention the tag function before the template literal so that, the tag function gets associated with it. name = ; color = ; message = introduce ; const 'Joe' const 'green' const `Hello, I'm and my favorite color is ` ${name} ${color} Please note the tag function introduce before the template literal. Tag function takes arguments The first argument of a tag function contains an array of string values. The remaining arguments are related to the expressions. { .log( , strings); .log( , arg0); .log( , arg1); ;
} name = ; color = ; message = introduce ; ( ) function introduce strings, arg0, arg1 console 'strings' console 'arg0' console 'arg1' return 'introduce...' const 'Joe' const 'purple' const `Hello, I'm and is my favorite color!` ${name} ${color} The argument strings is an array of all the strings in the template literals and both arg0 and arg1 represent the name and color values here. Output, Passing the expressions as individual arguments is not so great. Think about it, if there are 10 expressions in a template literal. We can make use of JavaScript rest operator(...values) to collect the arguments as an array. { .log( , strings); .log( , values); ;
} name = ; color = ; message = introduce ; ( ) function introduce strings, ...values console 'strings' console 'values' return 'introduce...' const 'Joe' const 'purple' const `Hello, I'm and is my favorite color!` ${name} ${color} In this case, both strings and values are arrays. The strings argument contains all the strings where the values argument contains all the expression values. Output, Now, we can do everything possible with these strings and expression values to manipulate them. The desired output To get the desired output after the string manipulation, we will write a small logic inside the function. introduce unction introduce(strings, ...values) { msg = ; msg;
} name = ; color = ; message = introduce ; .log(message); let `<span style="color: ">
           Hello , Have a Nice Day! We know your favorite color is <u> </u>
      </span>` ${values[ ]} 1 ${values[ ]} 0 ${values[ ]} 1 return const 'Joe' const 'green' const `Hello, I'm and is my favorite color!` ${name} ${color} console We create a new template literal using the expression values and wrap it with the span element. Please notice, we have added a style to the span element to color the text as well. Output, Hello Joe, Have a Nice Day! We know your favorite color is green < = > span style "color:green" < > u </ > u </ > span Now, if you use the above output to add as innerHTML you can render it in the browser. .body.innerHTML = message; document Output, The text color will change as and when you change the color variable value in your code. Did you know 💅? If you are familiar with , you probably know about the . But, did you know, styled-components are created using tagged template literals? reactjs styled-component Yes true. Notice the syntax of a button created with the styled-component, Button = styled.button const `
  background-color: papayawhip;
  border-radius: 3px;
  color: palevioletred;
` Does it look familiar to the tagged template literal we learned? Read this awesome article to know more about it. The magic behind 💅 styled-components Conclusion Tagged Template Literals are powerful and the usage will vary from one application to another. At the same time, if you were new to it before reading the article, lookout for opportunities to use it. I have updated the with code examples. You may want to have a look. js-tips-tricks project in GitHub You may also like, My Favorite JavaScript Tips and Tricks Explain Me Like I am Five: What are ES6 Symbols? How to use JavaScript Collection with Map Everything you need to know about JavaScript Set JavaScript: Equality comparison with ==, === and Object.is If it was useful to you, please Like/Share so that, it reaches others as well. You can @ me on Twitter ( ) with comments, or feel free to follow me. @tapasadhikary Previously published at https://blog.greenroots.info/what-exactly-is-javascript-tagged-template-literal-ckg6hyekf000n8bs1hz9udvzc

What Is Tagged Template Literal In Javascript

What has worked for us in the past is a treasure trove that we are not leveraging enough Déjà vu I am trying to do something. I broadly know how to do it but I don’t know the exact details. I search for it on Google. Google is a very big name today because it lets you get access to relevant information very fast. But however there is one issue with Google search. There is too much of content. I call this content overload. For example let us say when I search “What is blockchain”, there are about 22,00,00,000 results that are available. How do I know what is good? I really don’t. But Google does a good job at showing the best results at the top. In fact a very good job at it and that is the reason Google has a market cap of . But still I am not happy many times as I can’t figure out which link works for me best. So what do I do? I go through couple of links and then finally zero in on the one that works for me. I follow the steps mentioned in the link and voila everything works. 847 Billion USD Great. Couple of days pass and I want to repeat what I did couple of days, weeks or months ago. I wish I had bookmarked that URL that worked for me. But I didn’t. I curse myself. Does the above situation sound familiar? I have gone through this feeling multiple times in last fifteen years or so after starting to use internet in my day to day life on a regular basis. But I keep finding myself in that position time and again. Why? I think two major reasons are Laziness and lack How about a personal Search Engine Any links that has worked for us in the past are a great resource for us in the future. If only we had collected all the links that had worked for us in the past we would all have our own battle tested links which would form a powerful knowledge base for us. What if you could bookmark and tag all the links that you have ever read and found it useful? Wouldn’t that be a great resource over the time. We have been working on something similar for the past couple of months. I wanted to test run our own product and hence in the last six months I have extensive used our product. I have it installed on all the systems I use. Whenever I read anything online I make sure to highlight the relevant parts. Most of the times I just stick to highlighting as it doesn’t disturb my reading flow. We also have the option of tagging and adding notes in our extension. My reading flow is of almost importance to me. So I generally only highlight the relevant content and keep reading. Once I read the end of an article I click on each of the highlights and add relevant notes and tags, so that I capture the context. Every once in a while I use the search query to find out all the highlights that don’t have tags and tag them accordingly. By doing this I have built a very powerful knowledge A tagged searchable knowledge repository for myself. I am really excited about this feature and possibilities it throws up. I have been doing this for the last couple of months and I can already see the value it has added to me. If you don’t trust me just checkout the number of articles I have been able to complete on medium. I generally highlight and take notes using our extension while learning or reading. I use our dashboard to extract relevant content when I am writing a blog. The best thing is that I am able to capture the micro context which I was not able to capture earlier. So whenever I sit down to write an article I don’t need to go back and read the full articles that I had bookmarked. Instead I can just breeze to the content that I had highlighted. This gives me a context of why I bookmarked this page in the first place. This way I have been able to reduce the number of to and fros and this has directly contributed to me writing and sharing more of my learnings. Automated Bibliography In most of posts I generally make it a point to link to the relevant blogs and videos that Inspired my learning and understanding. I was thinking it would be great if I could share more notes about my learning process itself when I right a blog. So we recently added a feature to our product where you can share your public highlights. So by combining the public highlights feature and search I will be able to share much more context along with the article. I personally feel this is a great way to share bibliographies of your posts. At learning paths we are betting heavily on search. Once you get a hang of our extension, I am sure you will be amazed by how many highlights you will create in a day. As the number of highlights grow, search will be the only forward. So just imagine this. When I share an article, I share the search query to all my public highlights that were relevant to that article. So now in addition to seeing which all articles I read in the process of writing the post you can also know the following. What are the exact parts of the articles that excited me. My tags for the selected content and my notes. How many notes I made in each of these articles. And let us say that in the search query I gave you, there were too many items. Now you can just improve the search query to find parts that are relevant to you. Microcontext In this era of content overload we need micro context. When there is lot of content out there, what we assimilate and digest becomes more important than the amount of content we read. Having tools that will help you capture the micro-context and organise your learnings are crucial. This is the gap that we are trying to fill with http://bit.ly/highlights-extension Do give it a run and let us know what you think.

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