Clarifai is a leading provider of artificial intelligence for unstructured image, video, and text data

Clarifai

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

Artificial intelligence (AI) is rapidly transforming how businesses operate and interact with their customers. From automating routine tasks to transforming decision-making processes,  AI has the potential to significantly improve efficiency and effectiveness in the business world. In this article, I will discuss how AI is shaping the future of business and provide examples of some companies that are already implementing these technologies. AI for Automation Automation is likely to be the only major way AI will shape the future of business. This has many implications for automating business activities and affects a wide range of industries. The following are a few of the most common: 1. AI Chatbots Chatbots powered by AI are computer programs that simulate conversations with human users via text or audio. They can be used to automate customer service, answer questions, and even make recommendations, all of which were previously done by humans. Chatbots can also be used in marketing as they can be programmed to interact with customers and provide personalized services. IBM's Watson, Amazon's Lex, Google's Dialogflow, and Microsoft's Bot Framework are some of the most well-known and widely used chatbot service providers for millions of businesses. 2. Voice Recognition AI voice recognition software is also broadly used. Customer service is perhaps one of the most common applications of AI-based automation that is already in place. However, it is also widespread in voice-enabled search and navigation, controlling home automation systems, hands-free app control, and other applications. To give you an idea, Nuance's Dragon, SoundHound's Houndify, and wit.ai are some of the most extensively used voice recognition service providers. 3. Robotic Process Automation In addition to automating manual, repetitive, and time-consuming tasks, robotic process automation (RPA) products also sped up workflows considerably. Among the examples are data entry, document processing, and customer service interactions. Some of the largest companies applying it into practice are UI Path, WorkFusion, and Automation Anywhere. 4. Image Recognition Artificial Intelligence image recognition allows computers to recognize objects, scenes, and patterns in images by analyzing and interpreting them based on AI algorithms. This technology has several applications, including object detection and classification - which are typically done by humans - and facial recognition and image search. Amazon's Rekognition, Google's Vision API, and Clarifai are among the most popular and widely used image recognition services today. 5. Autonomous Mobility Autonomous mobility refers to the use of AI to enable vehicles to operate autonomously, without the need for a human driver. This technology has the potential to transform the way people and goods are transported, resulting in increased efficiency, safety, and accessibility. AI autonomous mobility is achieved through a combination of sensors, cameras, and other detectors that allow the vehicle to perceive its surroundings. The vehicle then uses AI algorithms to process this information and make decisions about how to navigate its environment, such as adjusting its speed, changing lanes, and avoiding obstacles. One of the primary advantages of AI autonomous mobility is enhanced safety. Autonomous vehicles have the potential to significantly reduce the number of road accidents by removing human error from the equation. Besides, by optimizing routes and lowering fuel consumption, the technology can help to reduce traffic congestion and improve overall driving efficiency. Another advantage of AI autonomous mobility is increased accessibility for people who cannot drive. Autonomous vehicles, in turn, can provide a new level of independence and mobility to the elderly, disabled, or those without a driver's license. This technology has been developed by Tesla, Waymo, Easy Mile, and Boston Dynamics, to name a few. AI for Data Analysis AI is also increasingly being used in data analysis to assist organizations in making better, faster, and more data-driven decisions. AI algorithms can process massive amounts of data and identify patterns and relationships that human analysts may not notice right away. This data can then be used to inform business strategies, improve operations, and stimulate innovation. Some of the more specialized applications for these tools encompass: 1. Predictive Analytics With the help of historical data, AI algorithms can predict events in the future. Using AI, companies can predict customer behavior, sales trends, and market conditions more accurately. A number of AI algorithms are embedded in Netflix and Spotify's algorithms to help the companies better understand their users' likes and dislikes. Based on this data, services can then provide relevant recommendations to their customers to keep them engaged by listening to music or watching series on those platforms. 2. Fraud Detection Large amounts of data can be analyzed by AI to detect fraudulent activities such as credit card fraud, insurance fraud, and cyberattacks. The technology can detect unusual patterns of behavior and flag them for further investigation, improving financial transaction security and protecting organizations from potential losses. Master Card has long used AI technologies to provide a fraud score for every transaction on its network as part of its anti-fraud efforts, making the information available to its issuers. Moreover, AI is used to detect fraud in combination with Mastercard's biometrics and behavioral analysis capabilities. 3. Predictive Maintenance Data from equipment and systems can be analyzed by AI algorithms to predict when they may fail. This enables businesses to take proactive measures to avoid downtime and lower maintenance costs. Every year, General Motors spends more than $1 billion on maintenance and $7 billion on capital improvements. To reduce maintenance costs and unplanned downtime, the company now uses AI-driven tools to support the widespread adoption of predictive maintenance across its over 140 global production sites[ ]. 1 4. Supply Chain Optimization AI algorithms can analyze supply chain data to identify inefficiencies and make recommendations for improvement. There are several applications for this today: to analyze data such as sales trends, consumer behavior, and market conditions to predict future demand, allowing companies to better plan their production and inventory levels. Demand forecasting – to optimize inventory management by lowering the risks of stockouts and overstocking. Inventory management - to optimize delivery routes and reduce transportation costs by analyzing data such as traffic conditions, delivery routes, and shipping costs. Delivery optimization - to provide real-time visibility into shipment status, allowing businesses to quickly identify and resolve any issues that may arise. Supply chain visibility - to analyze data such as consumer demand, shipping costs, and market conditions in order to optimize product distribution while lowering costs and increasing margins. Blue Yonder, Aera Technology, Kinaxis, and IBM Watson Supply Chain are a few of the most recognizable companies in the supply chain optimization area. Product distribution - AI for Customer Service AI is being utilized more and more to improve customer service and provide a better customer experience. Artificial intelligence technology can assist organizations in increasing the efficiency and effectiveness of their customer service operations while also providing customers with timely and personalized support. 1. Virtual Assistants AI virtual assistants can provide personalized support to customers, assisting them in finding information or completing tasks. Virtual assistants, for example, can be integrated into websites or mobile apps to assist customers with their shopping experience. As such, Walmart customers can ask Google Assistant to add specific items to their shopping carts or learn more about their shopping habits in the app[ ]. 2 2. Sentiment Analysis AI can be used to understand customer sentiments and opinions by analyzing customer feedback such as social media posts and survey responses. This data can assist organizations in identifying areas for improvement and providing better customer service. For example, the global FMCG company Mars utilizes tools to assess video content and analyze sentiment in online ratings and reviews[ ]. 3 3. Automated Voice Response AVR tools are used to provide automated customer service over the phone and interact with customers by providing audio responses to their questions or requests. These systems can be programmed to respond to simple questions and requests, provide callers with information such as current balances, allow customers to access specific account information, and, when necessary, redirect customers to an operator or customer service representative. Alvaria, Avaya, and GeneSys are three of the most recognized companies in this field. AI for Marketing AI is being used to increase the efficiency of marketing efforts in a variety of ways. AI technology can assist organizations in better understanding their customers, boosting campaign targeting, and measuring the results of their efforts. It is used in marketing in the following ways: 1. Customer Segmentation To create targeted marketing campaigns, AI algorithms can analyze customer data such as demographic information, purchase history, and social media behavior. This enables businesses to better understand their customers and increase the effectiveness of their marketing efforts. The Amazon Personalize automated segmentation system, which matches customers with the most compatible products tailored to their personal needs, is one of the most vivid examples. This is achieved through the use of special techniques that learn how users interact with items and then segment them based on their preferences. 2. Social Media Marketing To better understand customer sentiment and preferences, AI algorithms can analyze social media data such as posts, comments, and likes. This data can be used to boost social media marketing efforts, such as by creating more relevant and engaging content. Content optimization By analyzing data to uncover patterns and trends in user behavior, this technology automates the process of personalizing content to target individual users. AI-powered content optimization tools can assist marketers in creating more personalized, compelling content to engage and convert customers by understanding user preferences. This technology also assists marketers in optimizing content for various channels such as the web, mobile browsers, apps, and others. Some of the most well-known companies in this regard are Marketo Engage, BridgtEdge, and Evergage. AI for Legal work In the legal industry, Artificial Intelligence is being used to automate repetitive tasks, improve the accuracy of legal research, and provide more efficient and cost-effective services to clients. 1. Contract Review and Analysis AI algorithms can be used to review and analyze large volumes of contracts, assisting lawyers in identifying key provisions and clauses in a timely manner. When compared to manual review, this can save time and improve the accuracy of contract review. 2. Document discovery AI algorithms can be used to categorize and prioritize legal documents, such as those used in litigation discovery. This can help lawyers identify relevant documents more quickly and make more informed decisions. 3. Legal Research Legal research tools powered by AI, such as Westlaw and LexisNexis, can provide lawyers with timely and accurate information on legal precedents, regulations, and case law. This can assist them in conducting more effective legal research and providing more informed advice to clients. 4. Document Automation Artificial intelligence algorithms can be used to automate the creation of legal documents like contracts and agreements. When compared to manual drafting, this can improve the efficiency and accuracy of document creation. Kira, Ross, Cognitiv+, LawGeex, Luminance, and Legal Robot are some of the best-known tools/companies developing and selling these technologies today. AI for Creative Activities Artificial intelligence is being used in the creation of music and video to automate tasks, generate new content, and provide artists and content creators with new creative tools. 1. Music Composition AI algorithms can generate new music by either creating original compositions or recombining existing elements in novel ways. For example, AI can analyse existing music to determine the key elements of various genres and then use this knowledge to create new pieces. 2. Video Editing AI algorithms can be used to edit video footage automatically, such as selecting the best shots, removing background noise, and color correcting. In comparison to manual editing, this can save time and improve the accuracy of video editing. 3. Content Creation AI algorithms can be used to create new types of content, such as computer-generated imagery (CGI) or motion graphics. This can provide new tools for artists and content creators to create and experiment with new forms of content. 4. Music Production AI algorithms can be used to automate music production tasks like mixing and mastering. AI algorithms, for example, can be used to optimise the balance of different audio elements in a mix, thereby improving the overall sound quality of a recording. 5. Video Enhancement AI algorithms can be used to improve video content quality by removing noise, stabilizing shaky footage, and increasing resolution. This can provide tools for content creators to produce high-quality video content with less effort. Amper Music, AIVA, Flow Machines, Synthesia, Elai, and DaVinci Resolve are among the most well-known companies in this field. AI to Identify Customer Needs Last but not least, Artificial intelligence is being used to identify customer needs by analyzing massive amounts of customer data and behavior in order to gain insights into customer preferences, motivations, and needs. One of the most visible examples of this is currently taking place in the fashion industry, with some examples provided below: This online personal styling service recommends clothing items to customers based on their personal style, body type, and preferences using AI algorithms. Stitch Fix: To determine which styles and colors are most popular, the global fashion retailer uses AI to analyze data from sales, social media, and customer feedback. The data is then used to inform their design and production decisions. Zara: This online clothing rental service employs Artificial Intelligence to recommend styles to customers based on previous rentals, preferences, and events they are attending. Rent the Runway: To inform their design and production decisions, this global fashion brand employs AI to analyze consumer behavior and preferences, such as the most popular colors, styles, and materials. Tommy Hilfiger: An Indian online fashion and lifestyle retailer employs AI to recommend products to customers based on their browsing and purchasing history, as well as their preferences and style. Myntra: Conclusion To summarise everything mentioned above, Artificial Intelligence is rapidly transforming the business world and shaping the future of many industries. AI is assisting businesses in making more informed decisions, enhancing efficiency and productivity, and providing a more personalized and effective customer experience by processing and analyzing massive amounts of data. AI is used in a variety of applications, including data analysis, marketing, customer service, and legal work. In a rapidly changing business landscape, the use of AI in these areas is aiding businesses in gaining a competitive advantage and staying ahead of the curve.
Still, despite the numerous benefits of AI, there are concerns about its potential impact on the workforce, as well as on privacy and security. As AI technology evolves, it will be crucial for companies to consider these issues and take a responsible and ethical approach to AI use. Overall, AI is profoundly and far-reaching shaping the future of business, and businesses that can effectively harness its power will be well-positioned to succeed in the years ahead. Lead image developed by stable diffusion.

AI's Growing Influence and the Future of Business

Branding is a multifaceted process that involves the creation and establishment of a unique brand identity in the minds of consumers. This identity is comprised of various elements, including brand messaging, visual identity, and experiential touchpoints that are designed to reflect the company's mission and values. In the realm of purpose-driven branding, companies seek to align their brand's purpose with the values and beliefs of their target audience, thus fostering a deeper emotional connection. According to the U.S. Census Bureau, the number of ecommerce companies in the United States has grown steadily over the past five years. In 2015, there were an estimated 1.3 million ecommerce companies in the United States. This number has grown to 1.8 million in 2020, an increase of 38%. The number of ecommerce companies is expected to continue to grow in the coming years as more consumers shift to online shopping. According to Statista, the global ecommerce market is estimated to reach . This is an increase of about 21% from 2017. In 2017, the global ecommerce market was estimated to be worth $2.3 trillion. This means that the ecommerce market has grown significantly over the past few years. $4.9 trillion by 2021 One effective means of conveying this purpose is through the utilization of active voice in brand messaging. By adopting an assertive and confident tone, companies can effectively communicate their brand's purpose and inspire their audience to take action in support of it. This can be achieved through a variety of tactics, such as crafting compelling brand stories, utilizing persuasive language, and issuing calls to action that motivate the audience to align with the brand's mission. How Branding Principles Can Be Incorporated In An Ecommerce Business: An ecommerce business should create an engaging brand story that resonates with its customer base. This story should encapsulate the company’s mission, values, and unique selling points. 1. Develop an Engaging Brand Story: Visual branding is key to capturing customer attention. An ecommerce business should focus on developing a consistent visual identity across all channels, including its website, social media, and marketing materials. 2. Utilize Visual Branding: Customer experience should be a priority for any ecommerce business. Companies should focus on creating a seamless experience for customers, from the moment they arrive on the website to the moment they receive their product. 3. Prioritize Customer Experience: Testimonials and reviews from existing customers can help build trust with potential customers. An ecommerce business should make sure to encourage customers to leave honest reviews and to feature these reviews prominently. 4. Leverage Social Proof: Quality content is key to any ecommerce business. Companies should create content that is informative and helpful to customers, and focus on providing high value content without any fluff. 5. Invest in Quality Content: As more and more customers shop online using their mobile devices, it is essential for an ecommerce business to make sure its website is optimized for mobile. Companies should focus on creating a mobile-friendly experience for customers. 6. Focus on Mobile Optimization: Branding is essential for an ecommerce business, as it helps to create a distinct identity and allows the business to stand out in the competitive online retail market. It is important to understand the branding essentials for ecommerce businesses that will help them to create a successful, recognizable brand. Defining Your Brand: The first step in creating a successful brand for an ecommerce business is to define the brand. This includes developing a brand identity, mission statement, and values. It is important to clearly define the brand's purpose and values, as this will help to create a distinct and memorable brand. Creating a Visual Identity: Once the brand is defined, the next step is to create a visual identity. This includes designing a logo, color palette, font, and other visual elements. It is important to create a memorable and recognizable visual identity that reflects the brand's values and mission. Developing a Brand Voice: It is also important to develop a brand voice that reflects the brand's personality and values. The brand voice should be consistent across all communication channels, including website content, social media posts, and emails. Creating a website: The next step is to create a website for the ecommerce business. The website should be visually appealing, user friendly, and feature the brand's visual identity. The website should also feature relevant content, such as product descriptions, customer reviews, and blog posts. Developing an SEO Strategy: It is essential for an ecommerce business to develop an SEO strategy. SEO is important for driving organic search traffic to the website, which can result in increased sales. SEO strategies should include keyword research, content optimization, link building, and more. Developing a Content Strategy: Content plays a key role in ecommerce branding. Developing a content strategy that focuses on creating interesting, valuable, and relevant content will help to engage customers and build brand recognition. Content should be distributed through various channels, such as social media, blogs, and email. Developing a Social Media Strategy: Social media is essential for an ecommerce business, as it can help to build a community of loyal customers. Developing a social media strategy that focuses on creating engaging content, interacting with customers, and responding to customer feedback will help to build brand recognition and loyalty. Analyzing & Optimizing: Finally, it is important to analyze and optimize the branding strategy regularly. This includes analyzing the website traffic, customer feedback, and other metrics to identify areas for improvement. Regular optimization will help to ensure that the branding strategy is effective and meeting the business' goals. Branding is essential for an ecommerce business, as it helps to create a distinct identity and allows the business to stand out in the competitive online retail market. Developing a brand identity, visual identity, brand voice, website, SEO and content strategies, and social media presence are all essential for creating a successful, recognizable brand. Regular analysis and optimization of the branding strategy is also important for ensuring that the strategy is effective and meeting the business' goals. Best Tools You Can Use To Build Your Ecommerce Platform From Scratch – WordPress is the most popular CMS that offers a wide range of themes and plugins to help in building an ecommerce website. 1. WordPress – Shopify is one of the most popular ecommerce platforms that offers a wide range of features and customization options. 2. Shopify – Magento is an open-source ecommerce platform that offers advanced features and customization options. 3. Magento – WooCommerce is a free plugin for WordPress that offers a wide range of features and customization options for ecommerce websites. 4. WooCommerce – OpenCart is an open-source ecommerce platform that offers a wide range of features and customization options. 5. OpenCart – PrestaShop is an open-source ecommerce platform that offers a wide range of features and customization options. 6. PrestaShop – BigCommerce is an ecommerce platform that offers a wide range of features and customization options. 7. BigCommerce – Volusion is an ecommerce platform that offers a wide range of features and customization options. 8. Volusion – Squarespace is an ecommerce platform that offers a wide range of features and customization options. 9. Squarespace – Wix is a website builder that offers a wide range of features and customization options for ecommerce websites. 10. Wix Use These AI Tools To Start Brand Online Chatbot platforms like Chatfuel, Botsify, Motion.ai, and ManyChat allow users to create and launch AI-powered chatbots for their brands. These tools can be used to automate customer service and sales inquiries, provide personalized product recommendations, and more. 1. Chatbot Platforms: AI-powered data analytics platforms like Google Analytics, IBM Watson Analytics, and Tableau allow businesses to track and analyze customer data. These tools can be used to gain insights into customer behavior and preferences, helping businesses better understand their target market. 2. AI-Powered Data Analytics Platforms: AI-powered image recognition platforms like Clarifai, Imagga, and Google Cloud Vision allow businesses to automatically identify objects, faces, and text from images. This can be used to create custom visual experiences for customers, helping them find the products they're looking for quickly and easily. 3. AI-Powered Image Recognition Platforms: AI-powered social media platforms like Hootsuite and Buffer allow businesses to automate social media posting and respond to customer inquiries in real-time. These tools can be used to manage customer engagement, boost brand visibility, and interact with customers across multiple 4. AI-Powered Social Media Platforms: Case Studies Of Ecommerce Brands Casper: One example of a successful ecommerce brand that is not commonly known is Casper, an online mattress company. Casper was founded in 2014 and has since grown to become one of the leading mattress companies in the world. Casper has successfully used ecommerce to reach a global audience and grow its business. Casper has built a successful ecommerce platform that has allowed them to quickly expand their reach and acquire new customers. They have implemented a range of strategies to drive customer engagement, such as creating a loyalty program, offering free shipping, and providing easy returns. Additionally, they have invested heavily in digital marketing to reach their target audience. Casper has also leveraged its ecommerce platform to create a unique customer experience. They have implemented a range of features such as sleep tracking, personalized sleep advice, and virtual try-on to help customers make informed decisions. Additionally, they have implemented a subscription model that allows customers to easily purchase and receive new mattresses on a regular basis. Overall, Casper has successfully used ecommerce to grow its business and become one of the leading mattress companies in the world. By leveraging its ecommerce platform, Casper has been able to reach a global audience and create a unique customer experience. This has allowed them to successfully differentiate themselves from competitors and establish a strong brand presence. Warby Parker: One example of a successful ecommerce brand that overcame the challenge of branding is Warby Parker. Warby Parker is an online eyewear retailer that has become a leader in the industry since its founding in 2010. The company was founded with the goal of making high-quality eyewear more accessible and affordable. Warby Parker quickly rose to prominence, but the company struggled to find a way to stand out from the competition. To solve this problem, Warby Parker developed a unique brand identity. The company created a fun and playful brand image, using bright colors and quirky visuals to set itself apart from other eyewear retailers. Warby Parker also created a strong social media presence, utilizing platforms like Instagram and Twitter to engage with customers and build relationships. The company also made sure to emphasize its commitment to social responsibility. Warby Parker donates a pair of glasses to someone in need for every pair purchased. This commitment to social good helped the company build a loyal customer base and establish itself as a leader in the industry. By creating a unique brand identity and emphasizing its commitment to social responsibility, Warby Parker was able to overcome the challenge of branding and become a leader in the ecommerce industry. Conclusion: Branding is an important part of any business or organization, as it helps to create a strong and recognizable identity that can be used to attract customers and build relationships. A strong brand identity helps to differentiate a business from its competitors, as well as to create an emotional connection with customers. Branding also helps to create a unified image across all marketing channels, and can help to create a consistent customer experience. One of the most important aspects of branding is developing a unique and recognizable logo. This logo will be used in all of the company’s marketing materials, from business cards to website designs. A logo should be simple and memorable, and should be easily recognizable by customers. It should also be versatile, so that it can be used in a variety of contexts. Another important aspect of branding is developing a strong tagline or slogan. This should be concise and memorable, and should accurately reflect the company’s values and mission. It should also be easy to remember and should be used consistently across all marketing channels. Creating a website is also an important part of branding. A website should be designed to be attractive and easy to navigate, and should include information about the company, its products and services, and its mission. The website should also include contact information and links to social media accounts. There are a number of free tools available to help businesses and organizations create and maintain a strong brand identity. Canva is a free graphic design platform that can be used to create logos, website designs, and other visuals. Hootsuite is a free social media management platform that can be used to schedule posts and monitor conversations on social media. Mailchimp is a free email marketing platform that can be used to create and send email campaigns. Branding is an important part of any business or organization, and can help to create a strong and recognizable identity that can be used to attract customers and build relationships. By using free tools such as Canva, Hootsuite, and Mailchimp, businesses and organizations can create and maintain a strong brand identity that will help them to stand out from their competitors.

Branding for a Purpose in 2023: An Ecommerce Guide

Marca con un propósito en 2023: una guía de comercio electrónico

Branding pour un but en 2023 : un guide de commerce électronique

2023 में एक उद्देश्य के लिए ब्रांडिंग: एक ईकॉमर्स गाइड

2023 年の目的のためのブランディング: e コマース ガイド

Branding com um propósito em 2023: um guia de comércio eletrônico

Xây dựng thương hiệu vì mục đích vào năm 2023: Hướng dẫn thương mại điện tử

2023 年的品牌推广：电子商务指南

Five benefits of attending hackathons My story Hey, folks. I have experience participating in hackathons and being a mentor at such events. Back in 2019, I attended by in London. This was an amazing event with more than 100 attendees. With this article, I wanted to share my experience, talk about my insights, and hopefully motivate you to attend hackathons in the future. SmallBizHack Intuit 1. You improve your soft skills, quickly If I’m honest, the main challenge of this hackathon for me was not related to engineering: it was about building a team. This required a lot of communication with many people at the event while trying to build relationships and trust quickly. I managed to organize a team of 4 people. With each team member being from a different country (Kazakhstan, Ukraine, India, Turkey), I had a chance to improve my intercultural communication skills on top of learning more about managing a team in an intense and fast-moving environment of a hackathon. https://twitter.com/sasha_khivrych/status/1099703955842629634?ref_src=twsrc^tfw|twcamp^tweetembed|twterm^1099703955842629634|twgr^1f9d6d8d813cba3b2858e33a71d7c348523fa662|twcon^s1_c10&ref_url=https%3A%2F%2Fpublish.twitter.com%2F%3Fquery%3Dhttps3A2F2Ftwitter.com2Fsasha_khivrych2Fstatus2F1099703955842629634widget%3DTweet?embedable=true It was a great opportunity for me to become better at listening to others, giving feedback, and accepting criticism. I feel like this experience made me a better leader. 2. You learn to brainstorm and make better product decisions I did not have a clear idea of what I wanted to build during the hackathon. A day before the event, I started brainstorming and decided it should be something for the hiring process. I then spent the first day of the hackathon refining product ideas with my team, starting with the problem definition and finishing with potential impact and benefits for our future clients. The final product vision was to create a platform that would automate and improve the hiring experience for companies and candidates. With a single platform, candidates would go through the entire interview process and eventually even be able to accept their job offer via our platform. From the company's side, they can build out interview stages with initial screen calls, quizzes, tasks, and scheduling face-to-face interviews. Moreover, this platform would contain a lot of templates for building interview stages and automation processes to analyze CVs, collect candidate progress and build leaderboards with candidates with the best results. As you can see, we came up with an interesting product to solve important hiring problems. A hackathon can teach you a lot about the product development process, just within the space of a few days. 3. You get exposed to new technologies One of the event conditions was to use QuickBooks API and Clarifai API or Vonage API (formerly, Nexmo) to win partner nomination. We were using all three APIs in our MVP. For example, we were using Nexmo to perform phone and video calls for interviews. One of the exciting features was to perform an initial screening phone call using Nexmo API and have a bot ask all the screening questions. Then the recording with answers was sent to Clarify API to analyze the interview result. It is quite a new experience to be interviewed by a bot, but we will probably see something similar in the future. Moreover, our platform used Quickbooks API as an accounting solution. I had not used any of these three technologies before the event, so I enjoyed exploring them and learnt quite a lot! Representatives from each company were there to support us and help us master the APIs quickly, which was another benefit of the hackathon. A hackathon can be a great excuse to play with cool and exciting tech that you aren’t exposed to in your everyday work life. 4. You meet new people in the industry Like with all public events, a hackathon is a great way to expand your professional network. I met many professionals from different business areas. It was exciting to speak with CEOs, software engineers, and designers and learn from them. I cherish the relationships I built during the hackathon and look forward to expanding my network even further during future events. 5. You (might) win I’m not going to lie: it feels nice to win! I was so happy when my team became the winning team of the Nexmo (now called Vonage). https://twitter.com/IntuitDev/status/1099728634896703490?ref_src=twsrc^tfw|twcamp^tweetembed|twterm^1099728634896703490|twgr^8ca4b2f881a41bdf1e7fd983e10eb0796f1e0ddf|twcon^s1_c10&ref_url=https%3A%2F%2Fpublish.twitter.com%2F%3Fquery%3Dhttps3A2F2Ftwitter.com2FIntuitDev2Fstatus2F1099728634896703490widget%3DTweet?embedable=true Winning at this hackathon was an important milestone for me professionally. I became even more motivated to improve my software engineering and leadership skills and keep working towards my dreams. By the way, you can also expect to win amazing prizes at most hackathons (money, tech gadgets, etc.). Moreover, I won a gadget just by taking part in a survey. There are plenty of opportunities to win prizes and cool swag at a hackathon. 😉 https://twitter.com/IntuitDev/status/1099726219338223617?ref_src=twsrc^tfw|twcamp^tweetembed|twterm^1099726219338223617|twgr^2b784b62e4dd0a142337b1a2ce7b83ba054e0f88|twcon^s1_c10&ref_url=https%3A%2F%2Fpublish.twitter.com%2F%3Fquery%3Dhttps3A2F2Ftwitter.com2FIntuitDev2Fstatus2F1099726219338223617widget%3DTweet?embedable=true Conclusion Learning from my experience, you can see that attending a hackathon is a powerful way to improve professional skills, build your career and expand your network. If you have never participated in a hackathon, I hope this article convinces you to try it. Originally published . here

You Should Participate in Hackathons; and Here's Why

Face recognition is a method of biometric identification through an image, video, or other audiovisual element of an individual’s face. Over the last decade, the global face recognition technology market has grown at an unprecedented rate. The needs caused by the pandemic also changed the dynamics in face recognition technology and accelerated the invention of new tools and solutions. Today, face recognition software is widely used in mobile banking, telemedicine, airports, hotels, and digital marketing. In this article we’re going to look at the biggest market players and provide a list of the best facial recognition software solutions so that you can choose the one that meets  your business goals. Best Face Recognition Software Providers: Our Top 9 1. FaceFirst FaceFirst is a facial recognition software platform designed to be scalable, fast, and accurate while maintaining the highest levels of security and privacy. FaceFirst provides surveillance, access control, mobile face recognition, and biometric data. The key features of FaceFirst include: Emotion recognition Age recognition Voice recognition Behaviour analysis Gender recognition 2. SenseTime This solution establishes a list of authorized drivers to facilitate enterprises’ automated management of employees and customers alike, lowering operational risk. Going beyond facial recognition, it also provides a customized driving experience through automatic seat adjustment, air conditioning set up, and multimedia system activation. SenseTime key features: 2D and 3D liveness detection Face matching and facial effects Gesture recognition Background segmentation 3. CompreFace CompreFace is a new and promising face recognition solution that can be easily integrated into any system without prior machine learning skills. It can be used to track the productivity of employees, control access to sensitive data and secure environments, and immediately identify visitors to give them top-notch customer service. CompreFace has made our list of best facial recognition software solutions for several reasons: It is open source and totally free The service can be set up and used without being an ML expert CompreFace uses one of the most popular face recognition methods with high accuracy The solution features a UI panel with roles for access control It starts quickly with one docker command 4. Trueface Trueface is a face recognition company that applies advanced computer vision technology to camera footage and images to enable businesses to make immediate decisions based on identified patterns. Trueface has developed a suite of SDKs and a dockerized container solution that harness the powers of machine learning and artificial intelligence to transform your camera data into actionable intelligence. Trueface key features: Face extraction and landmark detection Head pose estimation High precision modules for maximum accuracy Ability to operate completely offline 5. Clarifai Clarifai is a leader in artificial intelligence solutions, helping businesses and developers solve real-world problems with machine learning tools. It offers two powerful ways to integrate AI — Clarifai API and the portal. The face detection system returns probability scores based on the likelihood that the image contains human faces and coordinates locations of where those faces appear with a bounding box. Clarifai core features: Ethnicity recognition Gender recognition Layered security 6. Amazon Rekognition Amazon Rekognition makes it easy to add image and video analysis to your applications. You just provide an image or video to the Rekognition API, and the service can identify objects, people, text, scenes, and activities. Amazon Rekognition also provides highly accurate facial analysis and facial recognition. You can detect, analyze, and compare faces for a wide variety of use cases, including user verification, cataloging, people counting, and public safety. With Amazon Rekognition you can get information about: Facial landmarks, such as the position of eyes Detected emotions Age and gender Pose 7. Kairos Kairos has launched the Kairos Video Analytics Camera and Dashboard to help businesses get more customer data insights through customer identity and emotion detection. The Kairos Camera divides live videos into individual images and leverages facial recognition technology to collect customer identities. To ensure companies deliver a personalized experience to their customers, the device collects data about: Age and gender Time and customer status Emotions 8. Face X FaceX provides a platform for firms to easily implement Facial Recognition into their applications. Its versatility enables developers to integrate high accuracy face recognition APIs and SDKs with only a few lines of code. The main advantages of Face X are: High accuracy and better gender profiling Spoof detection Face tracking (useful if you need to monitor and track movements after detection) No racial discrimination Low cost 9. Paravision Paravision is the world's most advanced face recognition platforms, trained on an ever-expanding private global dataset of over 12 billion photos and videos. The software provides a comprehensive toolset for developing advanced face recognition solutions including face detection, face verification, face identification, and real-time streaming video. Paravision’s key features include: Face clustering Spoof detection Age estimation and gender detection Phenotype detection Emotion detection Behavior recognition capabilities What is the best face recognition software available on the market today? Making a strategic decision about what face recognition software to choose is not an easy thing. Each one includes unique features that suit specific business needs. But regardless of whether you’re a small start-up or a large-scale enterprise, face recognition technology can be the next step in improving your business efficiency.

Best Facial Recognition Software to Use in 2021

The term ‘curation’ is commonly associated with museums or libraries, not data science. However, much like the work that’s done on rare paintings or books, data curation tools make the most important data easily accessible to engineers as they build complex machine learning models. Without curation, data is difficult to find, analyze, and interpret. Data curation tools provide meaningful insights and enduring access to all your data in one place. In this article, we’ll dive into the importance of data curation for computer vision specifically, as well as review the top data curation tools on the market today. What is data curation? Data curation is the act of organizing, enhancing, and preserving data for future use. In machine learning, data curation describes the management of data throughout its lifecycle: from its collection and initially storage, to the time it is archived for future re-use. This process is all the more important for computer vision engineers, who deal with massive amounts of visual data on a daily basis. Instead of using manual methods such as writing ETL jobs to extract insights, data curation tools provide a streamlined way to access the right data whenever you need to. The importance of data curation for machine learning Under the hood, data curation tools directly influence computer vision model performance. Using data curation tools, engineers can get a better understanding of the data they’ve collected, identify the most important subsets and edge cases, and curate custom training datasets to feed back into their models. The best data curation tools enable you to: Make it easy to obtain insights on key metrics, as well as the general distribution and diversity of your datasets regardless of sensor type and format. Visualize large scale data: Quickly search, filter, and sort through the entire data lake by making all features queryable and easily accessible. Enable data discovery and retrieval: Identify the most interesting segments within your dataset, and manipulate them within the tool to create completely customized training sets. Curate diverse scenarios: The tool should fit well within your existing workflows and toolset. Seamlessly integrate: What are the best data curation tools for computer vision? With an overwhelming amount of AI products and platforms popping up year after year, how do you know which will provide the most value? Read on below to find out which data curation tool is the best fit for your computer vision project. 1. Aquarium Learning Aquarium is a data management platform that aims to make it easy to identify labeling errors and model failures. With Aquarium, users can version and combine model predictions with their ground truth. Aquarium is especially focused on curating and maintaining training datasets, catering less to raw data management use cases. This is because data exploration in Aquarium is predominantly tied to model predictions and ground truth labels. Users can access Aquarium via their cloud platform or API. However, they currently do not offer on-premise or VPC deployments, and there are no external integrations. - Aquarium supports image, 3D, audio, and text data. They also support multiple annotation types, such as classification, detection, and segmentation. Wide range of use cases - Users can manipulate evaluation thresholds and obtain interactive visualizations to obtain required samples quickly. Interactive model evaluation - Users can collaborate with each other on the Aquarium platform to build data subsets, associate them with issues, and identify new data for annotation. Collaborative features 2. FiftyOne Developed by Voxel51, FiftyOne is an open-source tool to visualize and interpret computer vision datasets. The tool is made up of three components: the Python library, the web app (GUI), and the Brain. FiftyOne does not contain any auto-tagging capabilities, and therefore works best with datasets that have previously been annotated. Furthermore, the tool only supports image and video data, and does not work for multimodal sensor datasets at this time. Unlike other tools, FiftyOne is designed to be used by individual developers rather than teams, functioning like a programming IDE. Today, the platform lacks collaborative features; for example, a single instance cannot host multiple user accounts. FiftyOne taps into TF and Pytorch dataset zoos to provide access to a variety of open datasets and open-source models. Model & dataset zoo - - Via the Brain, a separate closed-source Python package, users can quantitatively assess the uniqueness, mistakenness, and hardness of data. Advanced data analysis - FiftyOne directly integrates with popular annotation tools such as . They also have tight integrations with Jupyter and Colab Notebooks making it easy for users to run FiftyOne through Python notebooks. External integrations LabelBox , 3. Scale Nucleus Launched in late 2020 by , Nucleus is one of the newest data curation tools to hit the market. The Nucleus platform allows users to collaboratively search through image data for model failures. As of now, Nucleus only supports image data, with no support for 3D sensor fusion, video, or text data. Scale Users can access Nucleus via their cloud platform, API or Python SDK. Currently, Nucleus does not support on-premise deployability. - Users can search for visually similar images based on one or multiple base samples and associate custom tags with them. Visual similarity - Using the Nucleus SDK, users can create flexible metadata schemas. Nucleus provides smart methods to detect and create schemas using the annotation format provided. Metadata schemas - Users can create model entities and associate corresponding runs with them. Hence, models can be versioned based on runs (dataset & predictions). Model versioning 4. Clarifai Clarifai is an end-to-end solution for labeling, searching & modeling unstructured data. One of the first AI startups, they provide a platform for modeling image, video, and text data. While Clarifai’s original focus was enabling users to build custom models, they’ve recently added several data curation features including auto-tagging, visual search, and annotations. Ultimately, Clarifai is more of a modelling platform and less of a developer tool. They are best suited for relatively inexperienced teams getting started with ML use cases. - Clarifai offers a broad library of pre-built AI models, including anything from food to facial recognition. Ready-to-use model gallery - The platform supports for image, video, and text data. Wide range of data types - With the platform, users can customize or retrain existing models or create new ones from scratch. Model customization - In addition to their modelling platform, Clarifai offers fully managed annotation services through their Scribe LabelForce data labeling service. Data annotation 5. SiaSearch is a data management platform for computer vision data. Consisting of a scalable metadata catalog and query engine, SiaSearch enables developers to easily search through visual data, add metadata to frames and sequences, as well as assemble custom subsets of data for training or testing. SiaSearch With deep roots in autonomous driving, the SiaSearch platform is used by many OEMs, Tier 1s and tech companies. Aside from autonomous driving, SiaSearch also has solutions for robotics, retail, and more. - One of the only tools that can support 3D sensor fusion data, SiaSearch can analyze large volumes of unstructured sensor data, providing insights at the frame and sequence level. Specialized in sensor data - SiaSearch employs a large catalog of pre-trained extractors to automatically add frame-level, contextual metadata to raw data. Additionally, SiaSearch provides a toolbox for quick extractor development, allowing developers to integrate their own extractors. Auto-tagging capabilities - The SiaSearch platform features a unique, proprietary architecture that combines numeric and sequence-based queries to enable noticeably faster performance. Fast performance - Users can access SiaSearch via their web-based GUI or programmatic API. SiaSearch also supports cloud or on-premise deployment for enterprise users. Flexible workflows & integrations Interested in data curation? The right data curation tool can dramatically reduce the time spent on manual processes, allowing engineers to focus on what really matters - building great models. Lead image via Tobias Fischer on Unsplash Originally published by Clemens Viernickel on: https://www.siasearch.io/blog/best-data-curation-tools-for-computer-vision and has been reposted with permission.

5 Best Data Curation Tools for Computer Vision in 2021

Lessons learnt from going the full mile. Pet to Prod — Illustration by yours truly As someone who loves experimenting with new ideas and , I find myself working on side projects quite often. Sometimes for a day or a week, then I quickly lose interest and move on to the next burning desire. The projects don’t really get abandoned, rather, I convince myself that the necessary skills have been learnt…but this is not true! tools A few days ago, I was working on that automatically tags a picture using classification engines powered by . After successfully setting up the handlers, user interface, and confirming that everything works on my local machine, the desire to call it done and move on to something else rose again…but not this time, this one was going all the way to production. this simple Go project Clarifai API How do you hone good skills if you continuously practice bad form? A simple Go app, a few hundred lines of code, with only one job…how hard could it be? Well, quite hard. There is a myriad of things developers hand-wave with the excuse. You’ve probably seen a bunch of tutorials where API endpoints and secrets are hardcoded into the program. How do you hone good skills if you continuously practice bad form? So, I was going to do things the right way and take this project the full mile. Ended up learning a couple of lessons. “you would not do this in production, but this is just an example” Lessons Learnt tagifai.herokuapp.com Tagifai | What's in your photo? Automated photo tagging using Clarifai’s Image Classification Neural Networks. . Writing an article about what you did is kinda good. Perhaps with videos showing how it works. But what should be the crowning moment of your efforts is actually getting it in the hands of real users! Side projects often solve a user problem, so if your ideal users cannot engage with your side project, you failed. And no, a Github repo is not enough because the regular Jane will rather not clone a repo — even if she is a developer. Demo or it didn’t happen Assume the whole internet (well, maybe a few friends) will be checking out your work. The least you can do is a detailed README outlining the purpose of the project and how to get it running. Being a small project is not an excuse, that should actually make the documentation easy. Clear documentation is not optional, it is mandatory. How do you know your magic port on your local machine will be free in production? You don’t. Environments like Heroku randomly assign ports to your process (by default), is your program written to account for this? Basically, there are multiple factors that can make your perfectly running local application a brick in production, and there’s only one way to find out. It is not deployable code until it is running in production. Compared to platforms like , is still an infant. Node has established tools, documentation, and tutorials on how to do almost anything on platforms like Heroku. I found the lack of updated guides on deploying a Go app to Heroku quite frustrating. You don’t see this part if you never try to deploy your app. You face the karma of platform immaturity. Node.js Go dep-based It is one thing to write code and another thing to deploy it. There are conventions, best-practices, and tools (heck even git commands), that you might be totally oblivious to until you try to send you program to production. Some organizations have a DevOps team (or that one person who knows all about production :), so “mortal” developers are far removed from the realities of deployment like managing SSL certificates. It reveals gaping holes in your knowledge. Action Items Clearly, going the full mile with a side project is demanding. So, to improve my chances of success, I will be tackling future projects with these guidelines. Only work on projects that have a well-defined user. In other words, they do only one job well. Spend a day scoping the entire project based on your current experience with the required tools. If you can’t ship a (MVP) to production in 2 days, you should probably pick something smaller. Minimum Viable Product Develop code with the awareness that this is a bonafide production application. That means good documentation, proper security measures, and defensive programming. Evaluate the success of side projects by getting a few target users to try it out. This could be your buddies at work or followers on Twitter. Keep every side project running and functional. Avoid having a Github repo that “used to work” 2 years ago (guilty of this myself) — if you won’t support it anymore, then it is best to take it down. And that’s it! If you enjoyed this article, please give me some claps so more people can improve their side projects. Perhaps you have some other lessons to share, do leave a comment. Thanks!

Ever shipped a pet project to production?

From simple text-based services to chatbots, we’re spending more and more time on messaging apps. Beyond 1:1 and group chat, messaging apps are growing more innovative, more powerful, and have become a linchpin for customers, businesses, and teams across every industry and vertical. As a result, we’ve seen a flood of new messaging applications of all shapes and sizes entering the market. In fact, in monthly users, and the gap continues to grow. More and more functionality is taking place directly in the chat interface outside of sent and received messages. messaging apps have surpassed social networks So how do you set yours apart from the rest? To do so, you need a certain set of core, established features that users have come to expect. Without those, your app is doesn’t stand a chance. But you want to take your’s to the next level, differentiating it from the thousands of chat apps out there, or build messaging into your existing application to deliver a better user experience and drive engagement. In using the vast number of incredibly accessible and affordable APIs, SDKs, integrations, and infrastructures-as-a-service, you can do just that. In this post, we’ll start with features every user expects, then move into innovative ideas for building chat of the future. Expected Features of Any Chat App These are the fundamental features that every chat has (or should have) today: Send and receive messages in realtime, either one-to-one, or one-to many. Join and leave a chatroom. Messages should be stored and easily retrieved, either through a option or infinite scroll. Load more messages Messages should be time stamped. A user list (eg. buddy list) that updates in realtime as users go online/away/offline. Typing indicators to show when someone is actively typing. Alerts for offline users via push notification when they receive a message. Only authorized users to have access to channels to keep the conversations private when necessary. Ability to invite other users to a group or kick out/block them from a group for inappropriate actions. .gifs. So many .gifs. Emojis 😀 Chat Features to Set Yourself Apart Obviously, we’re all very familiar with the features listed avobe. Give or take, they’re apart of basically every messaging application out there today. So let’s get into why you’re really here, a list of messaging features that set your messaging app apart. These kinds of features are redefining the way communication takes place and continues to push the space forward. Programmability Before getting into specific differentiators, it’s important to first discuss how important it is to build your chat application with programmability in mind. What exactly is ? programmability Programmability means your chat app is “event-driven”, which gives you complete control over what happens in between a message being sent and a message being received. This could be adding business logic, alerts, or triggering 3rd party APIs. This design drives to innovative features listed below. Geolocation Instead of forcing users to switch between messaging and mapping apps, deliver geolocation directly to chat users. Cotopaxi delivers geolocation delivery updates directly in chat. Cotopaxi delivers geolocation delivery updates directly in chat. Embed live maps with realtime geolocation tracking directly into a message stream. eCommerce and on-demand companies can utilize this to allow customers to monitor their delivery or service in realtime. Integration examples: Mapbox, Esri, Google Maps Initiate directions and ETAs and publish them directly to the message stream. Combine with text-to-speech to get audio directions as well. Integration examples: Mapbox, Esri eCommerce and On-demand Run an entire shopping experience, from browsing to fulfilling orders with chat. Cotopaxi sends order updates via chat. Cotopaxi sends order updates via chat. Utilize chatbots with automated and AI-powered cognitive capabilities to assist shoppers with browsing, ordering, and troubleshooting orders from a messaging interface (more on chatbots later in the post). Stream order updates and alerts, like push notifications and SMS, as the order moves through the fulfillment lifecycle. Clicksend Integration examples: (SMS), Infobip (SMS), RingCentral (SMS), SendGrid (Transactional Email) Moderation There’s a lot of trolls out there. Chat apps today need moderation tools in them to filter messages on the fly, not only for text, but also for images. Moderate content flowing through a chat as the messages are published on the fly. This could filter out inappropriate messages, block trolls, or even remove competition mentions (for live events and launches). Integration examples: SiftNinja, Neutrino Moderate images on the fly using image recognition to filter out provocative images. Integration examples: Clarifai, Sightengine, AICeption Live Events Chat can compliment a video or live event, and allow users to interact with one another. Combine a chat application with a live streaming video, and showing how many users are currently viewing the live event. Collect and aggregate geolocation data of users to gain insight on how many people around the world are watching the event. Allow users to vote on custom polls to increase user engagement. Add interactive features like allowing users to raise their hands or upvote other messages. User Experience Build delightful user experiences for users. Send larger files of any format in realtime. Rather than sending the entire file, efficient chat applications send a reference to where the file is hosted for the end user to download. One should be able to bookmark/star important messages and see them later on. Message search and message tagging/pinning. Automated and AI-powered Chatbots Chatbots are difficult, but luckily for you, there are a ton of powerful chatbot services on the market today. You should be able to build your chat microservices to power your chatbots, and integrate 3rd party chatbot services directly into your application. Poncho delivers weather updates via chatbot in a chat app. Poncho delivers weather updates via chatbot in a chat app. Use automation to send responses when a situation is predictable. For example, a user starts chatting with a support agent on a website. While an agent may not be available immediately, you can collect user’s information like email as automated response saying, “Our agents will reply back in a moment. Please provide your email”. This benefits user as they won’t have to wait for long to hear back from agent and frees up time for agents. Use automation to send responses when the data is available and accessible. In the example of an ecommerce website, the user can type in a chatbox, “I would like to know the status of refund for my order”. The intent of this sentence is “refund status”. An automated response could be sent to the customer in with a mini form like “Please provide order ID and your email”. This enhances the user data collection experience. We can make users’ experiences better by asking questions and giving automatic responses contextually depending on where the user is on the site. While they are on the payment page, you can show them a personalized coupon that they can use. Combine activity history with customer inputs to make recommendations and answer questions in realtime. Integrations and APIs This is similar to chatbots, but more broadly speaking. However, the technology is related, in that you should be able to build your own chat microservices to integrate 3rd party APIs. Interact with 3rd party apps and APIs using commands (like ) to make your tasks more efficient. Slack apps Utilize 3rd party alerts and notifications services to trigger SMS, mobile push notifications, and browser notifications. Initiate a video call or interactive collaborative environment with services like Cisco Spark. Create your own API which can be called from chat using a command that fulfills your needs only. Analytics You should give your business users full transparency into the usage of their application. For customer support types of messaging apps, allow business users to see average chat time, connected end users, open and closed chats, or even sentiment ratings of conversations (using APIs like Watson Natural Language Processing). Monitor geolocation of connected users to gain a better understanding of where your users are located. Looking Forward We’re seeing chat move from simple messaging to widespread adoption across every vertical, from healthcare, to eCommerce, to live events. It has become a linchpin of human communication. In our next post, . we’ll show you how to build these features yourself, using our brand new ChatEngine Download eBook here! If you’re looking for a in-depth look into chat, look no further than our new eBook: In it, we cover: Chat is More Than Hot Air — How to Build the Digital Future . the different flavors of chat, choosing the right technology from what’s available on the market today and a comparison of build vs. buy and hosted vs. open source. The current landscape of chat technology: a view into how chat apps are changing from simple messaging to transform how industries and users communicate. How chat is evolving: a look at what features and experiences can be delivered to set yourself apart from the rest. How to ensure you’re at the forefront of innovation: Originally published at www.pubnub.com .

CLARIFAI

clarifai.com

-- employees

Since n.d.

COMPANY RANKING

CLARIFAI

EVERGREEN INDEX #3235

Representing this company? Click here to claim and customize this page!

Clarifai WIKI

Apply to get your Tech Company News Page live on HackerNoon.com today!

CLARIFAI

clarifai.com

-- employees

Since n.d.

COMPANY RANKING

CLARIFAI

EVERGREEN INDEX #3235

RELATED COMPANIESRANK

STORIES ABOUT CLARIFAI

Representing this company? Click here to claim and customize this page!

Clarifai WIKI

Apply to get your Tech Company News Page live on HackerNoon.com today!