Casetext

casetext

A Machine Learning Text Classification Case Study with a Product-driven Twist

একটি পণ্য-চালিত টুইস্ট সহ একটি মেশিন লার্নিং পাঠ্য শ্রেণিবিন্যাস কেস স্টাডি

Eine Fallstudie zur Textklassifizierung durch maschinelles Lernen mit einer produktgesteuerten Wendung

Un estudio de caso de clasificación de textos con aprendizaje automático con un toque impulsado por el producto

Une étude de cas de classification de textes d'apprentissage automatique avec une touche axée sur le produit

उत्पाद-संचालित ट्विस्ट के साथ एक मशीन लर्निंग टेक्स्ट वर्गीकरण केस स्टडी

製品主導型のひねりを加えた機械学習テキスト分類のケーススタディ

제품 중심 트위스트를 사용한 기계 학습 텍스트 분류 사례 연구

Um estudo de caso de classificação de texto de aprendizado de máquina com um toque orientado ao produto

Пример классификации текста с помощью машинного обучения с учетом особенностей продукта

Ürün Odaklı Bir Yönlü Makine Öğrenimi Metin Sınıflandırması Örnek Olay İncelemesi

Nghiên cứu điển hình về phân loại văn bản học máy với khuynh hướng hướng đến sản phẩm

产品驱动的机器学习文本分类案例研究

The sheer magnitude of generative AI mania on Wall Street can be difficult to quantify, but for one of the industry’s star stocks, the age of the GenAI boom has been transformative. Let’s try to make sense of the extent of the generative AI frenzy that had been a leading driver for growth throughout the S&P 500 in 2023. According to a Market Intelligence review of S&P 500 transcripts, collective earning calls mentioning AI throughout the year grew 10 times that of 2022, spanning in total. almost 2500 mentions There’s good reason for this. Bloomberg Intelligence recently suggested that the generative AI market could by 2032, representing a break-neck CAGR of 42%. grow into a $1.3 trillion industry There’s tangible evidence that such pie-in-the-sky figures could be grounded in reality, too. For semiconductor manufacturer Nvidia, the growth the company experienced on Wall Street in 2023 stood at 239%. The stock shattered the $1 trillion market capitalization milestone for the first time last year and now stands comfortably above $1.5 trillion. When zooming out to Nvidia’s market performance over the past five years the firm’s achievements appear all the more impressive. With over 1,600% growth recorded since 2019, the stock has become one of the major beneficiaries of the hype sweeping across Wall Street for the emerging technology. Although Nvidia’s exponential growth isn’t exactly an anomaly given the widespread growth driven by the generative AI boom, the semiconductor leader stands as a key example of the magnitude of industry growth that those adopting the technology could achieve. With this in mind, could another stock replicate Nvidia’s impressive rally in 2024? The answer to this could be determined by which tech giants move most decisively in adapting to the age of GenAI. Looking to a GenAI M&A Supercycle One of the key trends in the generative AI boom has been the emergence of innovative studios like OpenAI in the development of cutting-edge technologies. Notably, the current star of the GenAI landscape is ChatGPT, a large language model (LLM) that’s accumulated around . Owned by OpenAI, ChatGPT has formed the cornerstone of the generative AI gold rush. 180.5 million users to date While there’s much talk of an OpenAI initial public offering (IPO) at present, the company has received significant backing from Microsoft, with suggestions of the company to the startup. committing $10 billion Rather than seeing innovative GenAI startups launch IPOs, it seems more likely that leading ventures will be acquired in a widespread gold rush for leading tech companies to add the landscape’s most promising players to their repertoire. We’re already seeing evidence of the GenAI merger and acquisition supercycle taking place, with Databricks and Thomson Reuters acquiring Casetext for $650 million last year. acquiring MosaicML for $1.3 billion It’s widely expected that M&A activity will continue to grow, particularly for artificial intelligence firms, into 2024 and this is likely to form a foundation for shaping the performance of leading companies in the industry throughout the year. However, Maxim Manturov, head of investment research at Freedom Finance Europe, has warned that an acceleration in mergers and acquisitions shouldn’t be taken for granted by speculative investors. While optimism surrounds the expectation of increased M&A activity in 2024, dealmakers will have to navigate a complex environment of economic, strategic and geopolitical factors,” . Manturov notes “Success in this environment is likely to depend on prioritising strategic considerations, assessing the impact of global trends on business models and leveraging transactions to drive transformational initiatives.” These complexities suggest that the generative AI market will be controlled by the most resourceful players in the industry in 2024 as economic headwinds continue to linger. But can stocks compete with Nvidia’s significant growth throughout the last year? Let’s look at three stocks that could be set to outperform the wider market: 1. Amazon (NASDAQ:AMZN) \Amazon’s market capitalization weighs in at a remarkably similar value to Nvidia at $1.7 trillion, and the tech giant is also focused on building AI infrastructure at an enterprise level. The company is busy creating AI-based services for its Amazon Web Services (AWS) cloud computing platform in a move that’s likely to facilitate far greater growth. With the announcement of Bedrock in September 2023, Amazon that enables clients to build and scale customizable AI models. While Bedrock is likely to face competition from other major tech firms like Microsoft and Alphabet as it grows, the natural links to AWS are certainly an advantage. established a strong platform As for the stock itself, AMZN experienced an impressive rebound off late 2022 lows that hadn’t been seen since before the pandemic. With to $170.0 billion in Q4 2023 compared to Q4 2022, the company’s growth is supported by strong fundamentals. net sales increasing 14% 2. Alphabet (NASDAQ: GOOGL) \Alphabet is one of the most advanced players in the generative AI landscape today. Having for the emergence of the technology over many years following the acquisition of the artificial intelligence research lab DeepMind in 2014, the technology has the potential to grow the company across numerous verticals, including autonomous vehicles. made preparations Crucially, Alphabet launched its own rival to ChatGPT in Bard back in early 2023 and has been working to refine the technology in the months that followed. Given how important Google is to Alphabet’s growth, the firm has a keen interest in harnessing the power of LLMs and generative AI tools to aid user queries and pioneer time-saving processes. With gross revenue in Q4 2023, we’re likely to see much of Alphabet’s resources allocated to becoming a pioneer in the generative AI landscape, and the growth of Bard is likely to be at the forefront of this push to become a market leader in the field. climbing 13% to $86.31 billion 3. Apple (NASDAQ:AAPL) \Apple has been an excellent performer in 2023 even despite the company avoiding the temptation to dive immediately into the generative AI furore on Wall Street. Instead, Apple opted to focus on the launch of the Vision Pro headset in early 2024, but more analysts have begun to anticipate the iPhone manufacturer’s long-awaited entrance into the market later in the year with a future upgrade for the iOS operating system on the way. Reports suggest that Apple is set to commit to generative AI investments, and this is likely to manifest itself as software programs intent on mimicking the human ability to learn, understand patterns, and make predictions. $1 billion per year These innovations will significantly improve the functionality of the iPhone and ultimately plenty of other Apple products which could transform the company’s fortunes following a from Barclays analysts. recent stock downgrade The Generative AI Boom May be Hard to Anticipate At this stage, it’s important to highlight that generative AI is a young technology that’s still emerging, and as such, it can be difficult to anticipate how the boom will shape up. The path from ideation to implementation will be a tricky one to navigate for many enterprises, particularly with costs remaining high across the semiconductor market. With this in mind, there’s every chance that innovations could arrive from left field to make the technology more cost-effective or practical for users. Despite this, it’s reasonable to anticipate that the tech landscape’s biggest players will be front and center as the generative AI revolution continues to dominate the sector. Whether they ramp up their M&A credentials or work on bringing their own LLM rivals to market, the likes of Amazon, Alphabet, and Apple are likely to have a major say on how the industry grows throughout 2024.

Assessing the 3 Best Generative AI Stocks to Compete with Nvidia in 2024

2024 সালে এনভিডিয়ার সাথে প্রতিযোগিতা করার জন্য 3টি সেরা জেনারেটিভ এআই স্টক মূল্যায়ন করা

Bewertung der drei besten generativen KI-Aktien, die 2024 mit Nvidia konkurrieren können

Evaluación de las 3 mejores acciones de IA generativa para competir con Nvidia en 2024

Évaluation des 3 meilleures actions d'IA générative pour rivaliser avec Nvidia en 2024

2024 में एनवीडिया के साथ प्रतिस्पर्धा करने के लिए 3 सर्वश्रेष्ठ जेनरेटिव एआई स्टॉक का आकलन

2024 年に Nvidia と競合する生成 AI 銘柄のベスト 3 を評価する

2024년 Nvidia와 경쟁할 3가지 최고의 생성 AI 주식 평가

Avaliando os três melhores estoques de IA generativos para competir com a Nvidia em 2024

Оценка трех лучших акций генеративного ИИ, которые смогут конкурировать с Nvidia в 2024 году

2024'te Nvidia ile Rekabet Edebilecek En İyi 3 Üretken Yapay Zeka Hissesinin Değerlendirilmesi

Đánh giá 3 cổ phiếu AI có khả năng sáng tạo tốt nhất để cạnh tranh với Nvidia vào năm 2024

评估 2024 年与 Nvidia 竞争的 3 只最佳生成人工智能股票

introduction of the DRC20 token standard has sparked interest and raised questions about its potential to compete with other token standards like BRC-20 and s LTC20. Dogecoin's Bitcoin’s Litecoin’ However, there is an interesting pattern that has followed the emergence of token standards recently, just like that of the cryptocurrency invention and launch. When Bitcoin was created, the Litecoin team took it upon themselves to create a “lighter” Bitcoin in the cryptocurrency. Dogecoin, on the other hand, decided to make a “joke” out of Bitcoin by creating the Elon Musk-admired asset. Now, there seems to be a repeat of the occurrence with the creation of the “20s” token. Read More About Bitcoin BRC-20 Token Standard Here As a meme, Dogecoin was developed without any concrete utility. However, the DRC20 experiment may change the notion, as the community seems committed to creating use cases for the eighth-largest blockchain in market capitalization. But there are significant differences in Dogecoin’s standard considers priority, when compared to Bitcoin and Litecoin. In BRC-20’s case, text inscriptions are the leader of the pack, rising above the widely-known images on the Ethereum blockchain. Litecoin, however, has followed in Ethereum’s footsteps with the creation of NFTs like the Litecoin Punks. But Dogecoin’s Doginals, inspired by Bitcoin Ordinals, has been marred with a hike in transactions, and a raging demand for better adoption. But what exactly is the DRC20? DRC20: Drawbacks and Potentials Instead of linking inscriptions to satoshis, the smallest unit of Bitcoin, Dogecoin inscriptions are tied to Shibes. For context, Shibes are considered the tiniest unit to measure the meme. Recall that Bitcoin had an issue of congestion when transactions surged amid the adoption of BRC-20. Some members of the Dogecoin community have also shown concern while looking for solutions to avert a repeat of the Bitcoin incident. However, the dedicated Twitter page of the DRC20 invention DogeLabs has addressed the worry. https://twitter.com/verydogelabs/status/1657380263532576770?embedable=true According to the developers, there were already solutions in place to cover the issue of indexer blockage. An indexer challenge occurs when it becomes difficult for transactions to be verified on a block. Therefore, resulting in clogging, and a hold-up in processing subsequent transactions. Despite the assurance of a solution, Dogecoin transactions surpassed that of Litecoin and Bitcoin. As of 14 May 2023, the transactions on the Litecoin network were over 452,000. Bitcoin recorded over 565,000 while Dogecoin came top with 719,895, based on data from . BitInfoCharts . Usually, an increase in transactions means a spike in volume. And when this metric increases, it implies that there have been a lot of coins that have participated in exchanging hands around a specific period. Thus, this infers that the introduction of DRC20 has had a positive effect on the Dogecoin ecosystem. But one should note the increased number of transactions does not necessarily translate to profits for participation. In terms of price, showed that the adoption of the token standard has had little to no effect on the DOGE value. At the time of this writing, the coin price was $0.07, consolidating from its value in the last seven days. CoinMarketCap Nonetheless, DRC20 has been one of the factors that have brought Dogecoin into the limelight. Whether this experiment lasts the test of time or not, largely depends on several factors including market sentiment, and developer dedication. Still, it remains a trial to keep an eye on especially in a market that isn’t really filled with enthusiasm.

Dogecoin’s DRC20: Ready to Give BRC-20 & LTC20 a Run for Their Money?

This may be almost a non-article, but the buzz surrounding , especially after the , makes it necessary for every self-respecting professional to try AI tools. ChatGPT announcement of AI-powered Microsoft Bing For those that live under a rock and avoid all the news (I deeply respect that lifestyle choice in this day and age), ChatGPT is a language model developed by OpenAI that can chat with users and generate human-like responses to text prompts.  It is trained on vast amounts of data and can understand and interpret natural language. I’ve used good old professionally before, mostly to help me compose take-home assignments, but since I don’t really work with code that much, I wasn’t too sure if ChatGPT would be such a game-changer for my workflow. GPT-3 Product managers, especially in startups, have to deal with a ton of different tasks on a daily basis: doing market research, generating new product ideas, thinking up new ways to gather customer feedback and then analysing it, and so on, and so on. Could at least some of the routine tasks be made easier with ChatGPT? Market Research I remember spending days looking for ways to find current market data at various start-ups. Sometimes that data was needed to confirm that the idea had some merit at all. Sometimes I needed good TAM, SAM and SOM figures. Can ChatGPT help us here? Getting the right numbers Let’s ask it for the TAM (Total Addressable Market) of the digital fashion market. One of these is absolutely useless (why online clothing rental?), but the other one is pretty good. However, since ChatGPT can’t provide you with any links, and some of this info may be coming from behind paywalls, finding the exact reports it mentions won’t always be easy. I still think that knowing where to look for this kind of info is an incredible power to have, even if the actual data you get from ChatGPT may have its issues. The verdict: Great way to start your research, but a very poor data source to fully rely on. Looking at products’ competitors ChatGPT is definitely not all-knowing, and while it can provide info on major companies and their competitors, the results are very much hit-or-miss. Here’s an attempt to get a list of competitors. I love Airtable and have used it for numerous projects. I am aware of the real competitors, so it would be very easy for me to judge the response. Airtable Since ChatGPT is operating with minimal knowledge of Airtable’s functionality, it focuses on some keywords, while ignoring the gist of it. Unfortunately, simple Google searches for “Airtable competitor”, “Airtable clone” or “Airtable alternative” won’t give you any easy answers either, because you’ll be bombarded with tonnes of ads for project management tools—one of Airtable’s use cases, but definitely not the only thing it could be used for. It’s a 0:0 in ChatGPT’s competition with traditional search engines. It’s good news that at least my job is safe here. By the way, the real competitors would be , an advanced database solution, and , an open-source Airtable clone. Budibase Baserow The verdict: Be very cautious with this info. It’s just as bad as blindly trusting the “I’m feeling lucky” button on Google. Content creation ChatGPT (even GPT-3, really) is supposed to be pretty good at writing stuff. Can it help us here? Product specifications Hear me out, this use case is absolutely AMAZING. While you won’t get your work done for you, ChatGPT will give you a great template to start from. It’s like doing a backlog grooming with a team member that knows absolutely nothing about anything. The verdict: This definitely works! Use the results as a template to help you think about the real requirements, user stories and acceptance criteria (depending on your team’s approach to writing specs). Can you generate UI Strings? I have no idea whether all product managers do this, but I often get involved in writing short phrases that would be easy to understand. Even given a more detailed brief, ChatGPT fails in a way even a junior designer never would be able to. The verdict: You can’t replace a copywriter or even a good product manager or designer who could write well, with ChatGPT. Don’t even try, you will fail spectacularly. Customer Feedback Product managers often need to work with customer feedback. If there is a functioning Customer Success team, we still need to analyse the incoming messages to get the prevailing themes and see what can be improved. If there isn’t, the Product team can often end up doing at least a part of customer support work. Can ChatGPT help here? Summarising the reviews Let’s test it out on some app reviews for a hip social networking app all my friends use. I only used the top 6, but you could feed in more info if you desire. The verdict: This is a great use case. Text summarization is a task that has already had some decent solutions, but ChatGPT does it really well, so you don’t have to rely on third-party services. Replying to reviews A good reply can persuade the user to improve their rating of your app. It also needs a great understanding of the context. Could ChatGPT be of use here? Not too bad! A bit too wordy for App Store, but it’s a good neutral response. If a negative review makes you angry, ChatGPT is a good way to clear up your mind. It’s pretty similar to the actual developer response that was posted, except that one was more laconic: The verdict: Probably won’t help with specific bugs, but a good way to generate unique neutral responses. Conclusion No matter what you read here, give ChatGPT and other AI tools, including Notion AI, a try. There are many use cases where you may find them useful, even if I didn’t. AI text tools won’t steal your job if you are competent, the same way Trello, Jira, Notion or whatever tools you use on the daily basis never did. However, if you won’t master them to amplify your work process, you will end up in the same position as artisans did when machinery came into their respective fields of work. Perhaps there will be a demand for artisanal organic product management in the future, but even if you plan to go down that alley it won’t hurt to learn more about your future enemy. I would love to hear more about your experience in using ChatGPT and other AI tools in day-to-day product management tasks!

Can You Use ChatGPT to Save Time Doing Day-to-day Product Management Tasks?

আপনি কি প্রতিদিনের প্রোডাক্ট ম্যানেজমেন্ট টাস্ক করার সময় বাঁচাতে ChatGPT ব্যবহার করতে পারেন?

Können Sie ChatGPT nutzen, um bei alltäglichen Produktmanagementaufgaben Zeit zu sparen?

¿Puede usar ChatGPT para ahorrar tiempo en las tareas diarias de administración de productos?

Pouvez-vous utiliser ChatGPT pour gagner du temps dans les tâches quotidiennes de gestion des produits ?

क्या आप दैनिक उत्पाद प्रबंधन कार्यों को करने में समय बचाने के लिए चैटजीपीटी का उपयोग कर सकते हैं?

ChatGPT を使用して、日々の製品管理タスクの時間を節約できますか?

ChatGPT를 사용하여 일상적인 제품 관리 작업을 수행하는 시간을 절약할 수 있습니까?

Você pode usar o ChatGPT para economizar tempo nas tarefas diárias de gerenciamento de produtos?

Günlük Ürün Yönetimi Görevlerini Yaparken Zaman Kazanmak İçin ChatGPT'yi Kullanabilir misiniz?

Bạn có thể sử dụng ChatGPT để tiết kiệm thời gian Thực hiện các công việc quản lý sản phẩm hàng ngày không?

您可以使用 ChatGPT 来节省执行日常产品管理任务的时间吗？

The job of attorneys and law firms is already changing in many ways thanks to artificial intelligence (AI) and machine learning, and there are a lot of exciting prospects ahead. So, let's talk about how legal companies have changed over time—both positively and negatively—thanks to artificial intelligence and machine learning, and how they may get better yet. Benefits of AI and Machine Learning in the Legal Industry Let's start by talking about the advantages that machine learning and AI provide for the legal sector. Automatic recollections Legal automation is performed by decomposing tasks or procedures that are typically handled by lawyers and embedding some of those components in technology. In high-volume data collecting, review, repeatable procedures, and document preparation, complicated, high-volume labor and frequent, repetitive tasks are the most often automated legal tasks. To properly address standard form letters and to keep track of court dates, think about adopting mail merge technology. However, the applications of legal automation have become more sophisticated, ranging from filtering enormous document review sets during litigation to extracting important terms from contracts in due diligence processes. This is another field of law where AI may be used to its advantage. Recently, lawyers have started automating the drafting of legal contracts and important steps in the negotiating process. Less time is required for contract evaluation and analysis. In order to provide clients with advice, lawyers must evaluate contracts in bulk and provide comments and red lines. Consider a company that has thousands of contracts that have been examined, and you need to preserve a record of the financial obligations, renewal, expiration dates, and other information. AI is capable of handling these jobs effectively in a legal company. The method for applying AI-powered document assessment is simple: The system finds the necessary documents, examines them, and finds the needed phrases and clauses, at which point they are prepared for review. The new technology requires careful project management while also assessing the uploaded materials. A combination of technology and manual labor is being used in this. In order to help with the practical assessment and classification of documents, AI-powered document review understands papers in a variety of languages, file types, and practice areas. As a result, staff may devote more time to quality control and legal analysis. Legal research is simplified. Lawyers and law companies today have unmatched access to data about attorneys, judges, courts, and damages, to mention a few. Anecdotes and first-hand knowledge have been displaced as the main sources of information by data-driven knowledge. All of the information will enable you to develop a strategy that is even more effective. These AI-powered tools make it easier for you to understand details of your case that were previously unclear, such as how frequently your judge allows motions for summary judgment or which court is best for your client's case. Your clients are interested in the cost of legal representation, the length of the litigation, and the likelihood of a successful resolution. You used to make the most of your resources, but you now have more. The same information, the same insights—everything that helps you create a winning case strategy also helps you instill confidence in your customers. More than just giving them the information they need, keeping them informed also requires building their faith in you and your client's future. Elimination of labor-intensive tasks Up to 23% of a lawyer's routine activities might be replaced by artificial intelligence and automation. Many of these tasks are mundane paperwork-based chores that take attorneys' focus away from the strategic requirements of a case. E-discovery, which involves examining digital data to gather non-privileged information about a case or claim, is the most fundamental and often utilized type of AI in law. E-discovery software allows lawyers to scan documents based on search phrases or criteria like dates or locations. Lawyers can obtain more essential information with this extra time. Similar to Kira Systems, AI systems allow attorneys to find, collect, and assess material from massive amounts of contracts. This facilitates the creation of merger and acquisition due diligence contract summary charts. The artificial intelligence (AI) program known as Casetext's Case Analysis Research Assistant, or CARA, analyzes citations and provides a list of particular proposed instances that aren't expressly acknowledged in the text. Using CARA, attorneys may quickly locate pertinent agencies and study cases that the opposing counsel is likely to mention. Using a proprietary algorithm, CARA scans legal documents, legislative provisions, opinions, and editorials, enabling the typical lawyer to conduct investigations more effectively without worrying about missing the best cases. Work of a high calibre Software innovation may deliver error-free work when it is not worn out or confused. The organization of a document may be improved, and it can be maintained for the course of the document's existence along with any internal cross-references. Using document comparison and artificial learning, contract comparison tools can find missing provisions, incorrect language, or unclear phrases in a single file as well as across related documents. Reducing legal practitioners' stress Lawyers must manage several clients, analyze paperwork, do legal research, proofread, and do other tasks. It's a fairly arduous task. The program assists in carrying out the necessary tasks so that attorneys may concentrate on original research. It raises the satisfaction of attorneys at work. Tools make duties easier to accomplish quickly and properly, and they also help attorneys save time and feel less stressed. For instance, the timely completion of key activities is greatly aided by correct scheduling of such jobs. You could benefit from using tools like email calendars if you're the kind of person who still forgets things even after writing them down. When deadlines or events are approaching, these calendars may notify you. I'd want to note that artificial intelligence technologies are automating and replacing an increasing number of legal positions as I wrap up this post. The only option for any is to adopt this new technology. Manual reviewing, on the other hand, is seen by some as being more accurate and advantageous for attorneys. However, because they can complete tasks in a lot shorter time, artificial intelligence systems are advantageous for doing a task as quickly as feasible. A task may be completed precisely and quickly using sound machines and software. law firm

How the Use of AI and Machine Learning Benefits Law Firms

Creating Custom Widgets in Flutter: App Bar, Drawer, and Bottom Navigation Bar

Each eCommerce website needs to have pricing information, and displaying prices via table can be the most suitable and visually attractive way. In this article, I would like to cover the features you need for creating a WordPress pricing table the best WP pricing tables plugins. Also, I’ll try out to build a WP table without a plugin. Table of Contents: WordPress Pricing Table: Features We Need WordPress Pricing Table Plugins with Cases How to Create Pricing Table Without WordPress Plugin? TL;DR: Which WordPress Pricing Table Plugin to Choose? WordPress Pricing Table: What We Need? Before we check WordPress pricing table plugins available on the market, let’s figure out what do we need for the pricing page: : Create the pricing page structure following your pricing policy (or plans you have). For instance, a subscription-based pricing model with three plans. Structure : Allocate the main features of each pricing plan to help the user choose the most suitable one. Features : If you want to highlight one of the pricing models/plans, don’t forget to focus on it. Highlighted plan : Of course, the pricing page should follow your brand guidelines, so don’t forget to style your pricing page too. Styling WordPress Pricing Table Plugins with Cases 1. Elementor Pro Pricing Table Elementor team 🌟 Developer: Elementor Pro pricing table widget helps to stylish and build your pricing table. This plugin allows styling heading, pricing section, buttons, ribbons, etc. : ✔ Pros Styling options Editing without CSS or HTML Sale mode available ✘ Cons: High price No options to buy widgets separately The elementor pricing table is available only in Pro version, the prices start from $49 per year for one website, or you can opt for the Agency plan for $999 per year. 💰 Pricing plans: 🎨 Usage examples: Source 2. JetElements 🌟 Crocoblock team Developer: JetElements plugin is one of Elementor's best addons, which gives you flexibility and creativity for building pricing pages. ✔ Pros: Highlight each plan features Tooltips, CTA buttons, badges, tags 40+ Elementor widgets ✘ Cons: No pricing table templates 💰 The free version is available, and you can download it from the . JetElements premium costs $24 per year or goes such subscriptions: All-Inclusive ($130-$265), Lifetime ($750) Pricing plans: WordPress repository 🎨 Usage examples: Source Source 3. Responsive Pricing Table 🌟 WP Darko Developer: Responsive pricing table plugin is the free WordPress plugin created for simple pricing pages and has basic features. ✔ Pros: 11 fields available Highlight any plan Toggle, tooltips, skins in the Pro version ✘ Cons: Can be too basic for Devs 💰 This WordPress pricing table plugin is free and has Pro version plans: a single website for $19 per year or unlimited websites for $49 per year. Pricing plans: 🎨 Usage examples: Pricing table example Responsive table example 4. Pricing Table Plugin 🌟 Supsystic Developer: This WordPress pricing plugin allows creating a pricing table from the template with a drag and drop builder. ✔ Pros: Preset templates Custom header, features list, button Import or export pricing table ✘ Cons: Templates available only in the Pro version 💰 You can use the Lite version from the WordPress directory or proceed with six premium plans starting from $46 for one website / per year. Pricing plans: 🎨 Usage examples: Pricing table example Pricing page use case 5. Kadence Pricing Table 🌟 Kadence team Developer: The Kadence pricing table plugin helps to create a pricing table from a template or start from scratch. ✔ Pros: Table importer Animation effects Responsive design, translation-ready ✘ Cons: No free version is available 💰 Kadence pricing plugin costs $35 per year for unlimited websites. Also, Kadence's whole bundle for $219 includes the pricing plugin. Pricing plans: 🎨 Usage examples: Kadence pricing table example Pricing table case 6. AR Pricing Table Plugin 🌟 Repute Info Systems Developer: AR pricing table is a lightweight WordPress plugin with 300 table templates. ✔ Pros: Compatible with most popular WordPress page builders Live preview Custom CSS guidelines ✘ Cons: Limited free version 💰 Free version is accessible through , or the premium starts at $27 on codecanyon. Pricing plans: WordPress.org 🎨 Usage examples: Example Example 7. WP Table Builder 🌟 WP Table Builder team Developer: This plugin allows creating pricing tables, comparison tables, etc. The Wp Table Builder is built with this plugin, and it’s always a good tone to develop your pages with your plugins. pricing table ✔ Pros: Responsive tables 7 elements for creating simple tables Import & export tables ✘ Can be too simple for advanced pricing tables Cons: 💰 Among the free WP Table Builder plugin version, there are three plans available: Starter ($49), Pro ($99), Lifetime ($299). Pricing plans: 🎨 Usage examples: Source Source 8. Visualizer 🌟 Themeisle Developer: Visualizer is a handy tool for creating tables and charts, including pricing tables. ✔ Pros: Cross-browser compatibility Responsiveness Live editing ✘ More about creating charts than pricing tables Cons: 💰 vary from Personal ($99) to Agency ($399) options. Pricing plans: Visualizer Pro plans 9. Essential Addons for Elementor 🌟 WPDeveloper Developer: Essential Addons tool is one of the well-known Elementor plugins. This is not only a pricing table widget but includes more than 50+ free widgets, inclusively a pricing table widget. ✔ Pros: Lots of styling options Easy of use Lots of widgets ✘ Cons: Works only with Elementor page builder 💰 As the pricing table widget is available in the free version, I don’t want to stop in pricing plans. Essential Addons Pro starts from $40 for one website. Pricing plans: 🎨 Usage examples: Source Source 10. NinjaTables 🌟 WPManageNinja Developer: Ninja Tables plugin is one of the top WordPress plugins on the market. ✔ Pros: 100+ tables styles, frontend editing Responsiveness Easy-to-use ✘ Tables exporting is available only in the premium version. Cons: 💰 There are 6 pricing plans (wow!), so please check the to choose the fitting one. Pricing starts from $49 annually. Pricing plans: pricing page 🎨 Usage examples: Use Case How to Create Pricing Table Without WordPress Plugin? If you are not a plugin lover and think additional plugin affects your website speed, I want to create a pricing table with clean WordPress (5.9 version). To start with, I have deactivated all plugins I have and want to Add New page. So, go to : Pages > Add New Name your page as you wish: Press plus icon and search for table block: Then you’ll see two options available: Table block and Pricing table pattern: Let’s try each option. Creating a pricing table with a Table block Firstly, let’s choose Table block and configure how many columns and rows my table will have: I want three columns (pricing plans titles) and five rows (prices and features). Now I have the table created and want to turn on the heading section in the settings: Adding some text before proceeding: I also want to add the button with the email address so that the user can contact me for payment details: And add minimum styling to my table and button: So, my simple pricing table is ready: Creating pricing table with WordPress pattern Choose the pricing table pattern instead of the block to build a pricing table from the pattern. And you’ll see a ready-made pattern with columns and text. You can edit columns, text, buttons, delete unnecessary things, etc. I’ve changed the text, chose the gradient background, and my results were something like that :D Not encouraging, I suppose. The problem was in my WordPress theme, I was using Hello by Elementor (I forgot about this), so I think it’s just breaking my styling. It’s not a problem, I’ve changed the theme to Twenty Twenty-Two, and everything seems to look clean now: Final verdict: Creating the pricing table without plugins in WordPress is suitable for simple websites (maybe personal/blogging), but still, there is a lack of “no-coding” customization and styling. TL;DR: Which WordPress Pricing Table Plugin to Choose? Thank you for reading till the end. To sum up, I want to share my thoughts about which WordPress pricing table plugin to choose. From my point of view, the best options are: For newbies and those looking for a simple pricing table, the best options are , , Responsive pricing table Pricing table plugin Ninja Tables For creating attractive & creative pricing tables use , , . JetElements plugin Essential Addons Elementor Pro What do you think? I hope this pricing table plugins list was helpful for you, and you’ve decided which one you’d like to choose for your next project. Don’t hesitate to share any thoughts or suggestions with me.

10 WordPress Pricing Table Plugins that Rock

A few months ago I tried to find information about doing some natural language processing with Javascript. There was not much of it. Most of the time, I stumbled upon tutorials on how to do so in Python. I'm writing this article in the hope to help someone do the same with Javascript. At least, try to. Javascript ecosystem is large, yet machine learning is mostly done in Python. For some custom(complicated) cases, you maybe will decide not to use Javascript. I'll explain why you may do so. I want to point out that I'm not a machine learning engineer. I'll cover simple cases with no deep explanations of the underlying algorithms. There are manageable cases when you may try JS packages that do the classification. In some others, and if you understand ML concepts, you may create custom models using TensorFlow.js. My case seemed simple. I wanted to classify potential business problems(opportunities) for my . I'll tell you soon how it went, once we cover the tools. Let's start with simple cases. Reddit advanced search tool Natural.js It's a package for Node.js that helps deal with natural language. It has many useful built-in helpers. For example, it can do a sentiment analysis from the box and without any setup. Let's install it: $ npm install --save natura Easy sentiment analysis, right? const { SentimentAnalyzer, PorterStemmer } = require('natural');

const analyzer = new SentimentAnalyzer("English", PorterStemmer, "afinn");
const result = analyzer.getSentiment(["I", "love", "cakes"]);

console.log(result); // 0.66 Yes, it's easy. is a transformation function that converts words to theirs . To their original form, simply put. We pass an array of words to function, but we may use built-in to do so automatically. PorterStemmer stems getSentiment tokenizers Where is a promised text classification, Lebowski? I wanted to show the simplicity of usage, without even training some complex algorithms. Now let's see how it deals with text classification. The package supports the and . They work differently, so try each one and see what fits your case better. Naive Bayes classifier logistic regression const { BayesClassifier } = require('natural');

const classifier = new BayesClassifier();

classifier.addDocument('buy our limited offer', 'spam');
classifier.addDocument('grow your audience with us', 'spam');
classifier.addDocument('our company provides a great deal', 'spam');
classifier.addDocument('I like to read books and watch movies', 'regular');
classifier.addDocument('My friend likes to walk near the mall', 'regular');
classifier.addDocument('Pizza was awesome yesterday', 'regular');

classifier.train();

console.log(classifier.classify('we would like to propose our offer')); // spam
console.log(classifier.classify('I\'m feeling tired and want to watch something')); // regular Usually, you need a lot of examples. With a small amount of them, any method you choose(this library or a custom model) will output not the best results. Pay vast attention to your data, it's a major element in text classification. Maybe Natural.js will cover your case and you can finish the reading. If you need a more custom setup(if you think so, review your data again), read further. Brain.js This library helps you to build neural networks. Natural works with more simple algorithms. Neural networks are many algorithms that work as one, simply saying. They reflect the behavior of biological neurons that are great at . recognizing patterns Now you can customize the algorithms. Specifically, you can build your own neural networks architectures - specify how many layers you need, activation functions, learning rate, and other parameters. This is where it gets trickier. There are no "gold rules" at building neural net architectures. The process greatly varies from a use case. We may use the default options in the cases like defining a color from RGB params: const brain = require('brain.js');

// Build a default neural net
const net = new brain.NeuralNetwork();

// This is where we specify our data: input and the result(output)
// the data is an array of examples(input and output).
// And then the network trains on them.
net.train([
  // we tell it: if "r" from RGB scheme is 0.03, and "g" is 0.7
  // then the output should be "black"
  { input: { r: 0.03, g: 0.7 }, output: { black: 1 } },
    
  // notice that we skip some values from RGB, in this case we
  // missed "g"
  { input: { r: 0.16, b: 0.2 }, output: { white: 1 } },
    
  // here we point out all the RGB values
  { input: { r: 0.5, g: 0.5, b: 1.0 }, output: { white: 1 } },
]);

// This is how we run the network to get a prediction
const output = net.run({ r: 1, g: 0.4, b: 0 }); // { white: 0.81, black: 0.18 } It's a powerful way to build such a network without understanding the underlying concepts, data normalization. Just point out a few examples and you're done. However, in reality, you need more examples for better precision. Transforming text to numeric vectors Now we're talking about data normalization. For text classification, we need to transform the text into numeric values because Brain.js doesn't have data transformation flow for regular neural nets, yet you may try it for . Why convert strings to numbers? Neural networks training is a process of many math calculations, which require numbers, not other data types. You might use raw strings, they would be converted to their numeric representations, however, not to the format you(and the algorithms) probably want. What these "algorithms" do is figure out the patterns of the input to build a function that can calculate the output based on the input. So it's important how you do this transformation. custom LSTM, for example you may propose is to convert every character to their numeric order in the alphabet. For instance, "a" is 0, "b" is 1, "c" is 2 and so on. Thus, we will have 26 possible values for every character. It means, the word "car" can be represented as . In this case, if your task is to classify text with many sentences, your input dimensionality becomes 2D, which isn't fine, because input should be 1D. We might flatten the 2D array, but then it gets delicate. It means, the text like this "I want apples" converts to "iwantapples"(and then to a numeric 1D vector). It may be fine, yet we're not sure the network recognizes a pattern there to classify correctly. The first option [2, 0, 17] The big problem with such an approach is that every character is seen by a net , not as a word. Thus, "car" is , and the resulting function(a set of functions that process the input) may "think" it's almost the same as "bar" - . It doesn't think, of course, but the pattern says so. Thus, it's difficult to retrieve any context, we just perceive every character independently. independently [2, 0, 17] [1, 0, 17] is to do the same, but for words. In reality, we retrieve context mainly from words, not by characters separately. Such an approach also simplifies the calculations: we don't need to convert 2D input into 1D and a neural network gets fewer numbers to process, which is a performance boost. To convert words to a number, we should figure out what numbers to assign to them. You may create examples of text you will be training on, tokenize it into words(omitting punctuation because it doesn't add context), make a dictionary of these words, where every one of them gets an ordering number. It's like adding words to a and their number is an order in which they appear in it. E.g. if I have a text "I want apples.", my dictionary is , where the word "I" will be assigned to 0, "want" to 1, and "apples" to 2. The second option Set ["i", "want", "apples"] We may optimize this approach by also words to their root form, e.g. "apples" become "apple" because the net doesn't need to know(except the use cases where your task is to classify singular or plural forms) whether it's a singular or a plural form, it's better to have a numeric representation for a word abstraction - apples("apple", "apples"). stemming It's the most simple method to vectorize text. Though, it also has problems. In cases, where you need your neural net to "figure out" the context by looking for a set of words, it's difficult because in the example above, "I" and "want" are placed as neighbors (0 and 1 accordingly), but they aren't similar, they mean different things. For example, "car" and "automobile" mean the same but can be represented as 14 and 8233 with this approach. Thus, your model may derive different results based on whether your examples have synonyms. is to use pre-generated vectors. The ones that were generated by processing a lot of texts and deriving which words are similar, and which ones are different. Thus, for example, a vector for "car" may be , and for "automobile" it may be . As you noticed, they're not single numbers, but vectors for every word. Thus, you get a 2D array for the whole text. I'd suggest you go with pre-generated vectors such as . The third option [0.45, 0.78, 0.97, 0.34, 0.87] [0.49, 0.73, 0.98, 0.33, 0.88] GloVe Getting back to Brain.js Now you know how to convert strings to vectors, you can use the library to help you. It has various types of . The one we saw before is the with backpropagation. This is where things get delicate too, again - in choosing the right network type. A feedforward net is a simple one that takes an input, does some calculations-transformations, and returns the results. It sees every input independently, it doesn't have a memory. It means it can't derive context from multiple words. If your task requires so, you better choose recurring neural nets such as or ( ). pre-defined neural networks feedforward neural net RNN LSTM see the Brain.js details on them TensorFlow.js This is a path where you decided you require more custom setup. This is a Javascript version of a powerful machine learning framework for Python. It allows you to build any models or use already created ones by the community. However, they don't have much. And their functionality of converting Python models to JS ones and vice versa doesn't work well enough yet. The code may look like this: const tf = require('@tensorflow/tfjs-node');

const data = {
    // assume we already have vector representations of the text examples
    inputs: vectorRepresentations,
    // imagine we have such 3 classes
    output: [0, 0, 2, 1, 2, 1, 0, 1],
}

// tensors are TensorFlow vectors to simplify the internal
// processing for the library
const inputTensors = tf.tensor(data.inputs);
const outputTensors = tf.tensor(data.outputs);

const model = tf.sequential();

// 1st layer: a 1d convolutional network
model.add(tf.layers.conv1d({
	filters: 100,
	kernelSize: 3,
	strides: 1,
	activation: 'relu',
	padding: 'valid',
	inputShape: [MAX_WORDS_LENGTH, GLOVE_VECTOR_DIMENSIONS],
}));

// transform 2d input into 1d
model.add(tf.layers.globalMaxPool1d({}));

// the final layer with one neuron
model.add(tf.layers.dense({ units: 1, activation: 'sigmoid' }));

// here are some tuning, read in the TF docs for more
model.compile({
    optimizer: tf.train.adam(LEARNING_RATE),
    loss: 'binaryCrossentropy',
    metrics: ['accuracy'],
});

// print the model architecture
model.summary();

// train the model
await model.fit(inputs, answers, {
    // the default size, how many inputs to process per time
    batchSize: 32,
    
    // how many times to "process", simply put
    epochs: EPOCHS,
    
    // the fraction of the inputs to be in the validation set:
    // the set, which isn't trained on, but participates in calculating
    // the model's metrics such as accuracy and loss
    validationSplit: 0.2,
    
    // shuffle inputs randomly to have a different starting seed every time
    shuffle: true,
});

// save the model to load in the future and run classifications
await model.save('file://./data/models/myFirstModel'); Here we built a model to do text classification for 3 pseudo-classes(0, 1, 2). We used a 1d convolutional network for the 1st layer. TensorFlow allows you to specify any amount of layers you want, set training epochs, validation split, choose different ML algorithms, activation functions for every layer, and many other options. Though, we need to know how to build ML models. If we don't, we may add anything, tune parameters, and won't receive good results. I went to TensorFlow.js for more customizability but spent months on adjusting a lot of stuff and didn't get great results. I learned many things along the way, but still, I'm not an ML engineer, so it's better(faster) to use models built by professionals and not create your own wheel. But if it's for fun, why not! Then, let's understand the code I wrote. I chose this architecture due to its performance: convolutional networks are faster for text processing and also they process input in a kind of context. They're mainly used in computer vision because they process input matrices, not just 1d arrays of numbers. So, for example, if you get an image, 100x100 px, a convolutional network may process 5x5 pixel window per time. Thus, some noise and details can be classified correctly. For text, it's almost the same - we need to take multiple words in a batch and don't process them independently. Thus, simplifying a model's job in recognizing patterns. I chose GloVe vector representations, so my input data was a 2D array of numbers, where every subarray was a word representation. The parameter in a convolutional network is responsible for the "sliding window" - those 5x5 pixels to process per time. In my case, I specified to . It means the network processes 3 vectors(3 words) per time. The param tells how many neurons you want. means how many "steps" to take per once when moving the "sliding window". For example, for the text "I want to eat apples tomorrow", the first batch is , the second batch is , the 3rd is , and so on. So, it moves by one word per time to the right. kernelSize kernelSize 3 filters strides ["i", "want", "to"] ["want", "to", "eat"] ["to", "eat", "apples"] General learnings I spent some time with Natural.js, then Brain.js, and TensorFlow. I went to the last one for custom configuration and spent a lot of time building custom models. It'd be better to use an already built model for text classification. However, I didn't find a good way to transform Python TensorFlow models into Javascript ones, that's why in the end I switched to a Python setup with HuggingFace. But my task wasn't so straightforward. I wanted to classify potential people's problems and pains: where someone hates using something or complaining about things. There were some things I learned while building custom models with tensorFlow.js I wish I knew earlier. Write your experiments in a logbook. You'll be building various models with various hyperparameters and it becomes difficult to recall what worked well for you and what didn't. Also, don't forget about the test set(assuming you have a validation one too). There is a lot of things to mention about building ML models. Here are some I highlighted in my logbook. I hope it saves someone's time in narrowing the search when troubleshooting. . If a validation loss starts increasing. It should be similar to but slightly higher than a training loss. If it's lower or almost equal to a training loss, a model requires more training. If training loss is reducing without an increase in validation loss then again keep doing more training. When to stop training . In most cases, if you have 100% train accuracy, you've probably greatly overfitted. Or, a model recognized a "false" pattern in your data. You have 1.0 accuracy ? A big topic. Here's some reference(not mine, but I can't find the source): Overfitting If validation loss >> training loss you can call it overfitting.
If validation loss > training loss you can call it some overfitting.
If validation loss < training loss you can call it some underfitting.
If validation loss << training loss you can call it underfitting. A higher than training loss validation loss means overfitting, a model learned the patterns that happen to be true in training data, but they're not in real-world data. If you have a too powerful model(the one that has too many parameters and not much training data, see ), review it again and simplify, because for me some of the models memorized the data, and thus, have greatly overfitted. model.summary() Another evidence of overfitting is that your loss is increasing, Loss is measured more precisely, it's more sensitive to the noisy prediction if it's not squashed by sigmoids/thresholds (which seems to be your case for the Loss itself). Intuitively, you can imagine a situation when the network is too sure about output (when it's wrong), so it gives a value far away from the threshold in case of random misclassification. . Accuracy or loss fluctuates some portion of examples is classified , which produces fluctuations, as the number of correct random guesses always fluctuates (imagine accuracy when coin should always return "heads"). Basically, sensitivity to noise (when classification produces random results) is a common definition of randomly overfitting The training loss at each epoch is computed on .  The validation loss at each epoch is computed on , so it is normal for it to be noisier. usually the entire training set usually one minibatch of the validation set Take care of your batch size. Sometimes it needs to be adjusted: Smaller batch sizes give noisy gradients but they converge faster because per epoch you have more updates. If your batch size is 1 you will have N updates per epoch. If it is N, you will only have 1 update per epoch. On the other hand, larger batch sizes give a more informative gradient but they convergence slower and increase computational complexity.

Text Classification in Javascript: Comparing Natural.js Vs. Brain.js Vs. TensorFlow.js

A primer to understand how technology is poised to disrupt law Note: Below content is informed by online research, reading, and interviewing law practitioners. Resources can be found at end of the article. Background Emerging technologies such as mobile, data analytics, and machine learning had started affecting industries and customers beyond urban consumers and tech-savvy enterprises over the last decade. Retail and healthcare are examples of domains that have benefited significantly from this trend. Yet, there remain industries where technology has only started to a breakthrough in a meaningful way. Law is one such industry. I wanted to identify opportunities for technology to create a meaningful impact and started researching. Being an outsider, I’d to: develop a basic understanding of the field and state of technology adoption understand the ecosystem and market, especially in India’s context This article deals primarily with the first point. A follow-up article may detail out insights from the market. Need for legaltech If you’re inclined to read about the importance of law in society, a Google search is a good enough starting point. Hence, I’ll skip talking about that. What is legaltech then? as the use of technology and software to provide legal services and support the legal industry. It’s similar in spirit to how Fintech and Healthtech have augmented their respective industries and continue to do so. Unlike them, the law has been significantly slow on the uptake. This is due to resistance from a traditionally conservative industry mixed with a lack of understanding and interest from technologists. Wikipedia defines it Conversations about technology implementation in law often drift towards replacing lawyers. That line of thought misses the point entirely. Instead, legaltech’s biggest promises are: augmenting work of law practitioners and subsequently improving their quality of professional life expanding access to legal services and justice for ordinary citizens and small businesses Technology can help lower costs and increase efficiency. This can lead to a positive-sum outcome for all participants. Breaking down law Don’t worry, we aren’t going to do anything illegal. What we’ll do is categorize participants and work in the legal industry. Harry Surden, in a at Stanford Law, identified three types of legaltech users: talk about AI and Law Administrators of law Practitioners of law Users of law In terms of work, a distinction that’s useful is one between the practice of law and the provision of legal services. The defines the practice of law as “the application of legal principles and judgment with regard to the circumstances or objectives of a person that require the knowledge and skill of a person trained in the law.” That’s a mouthful, ain’t it? Simply put, that’s work that requires legal education and expertise. Legal advisory, lawsuits, and negotiations are examples of such work. American Bar Association In contrast to the practice of law, legal services require a low level of legal knowledge. Non-lawyers can perform such tasks too. These include company incorporation, patent filing, or rental agreements. Even amongst practitioners, litigation and corporate law is a high-level distinction. Prologue — technology in law Before moving on to legaltech, it makes sense to look at how non-specialized software is having an effect on the field. The below examples are specific to India and may not necessarily apply elsewhere. The use of video conferencing has exploded over the last few months due to the COVID-19 crisis. Many law firms and corporate teams had started embracing it prior to that. Cisco Webex came up multiple times during interviews. For litigators, it has been an abrupt transition that’s taking some time to get used to. Judiciary is struggling with network infrastructure issues amidst the pandemic. Bandwidth issues plague e-courts and online hearings. The office suite is used extensively for tasks such as legal document drafting. Microsoft teams may be a path of least resistance for those looking to move to the cloud for collaboration. The last point above around the adoption of cloud collaboration software is interesting. It could be an example of a makeshift solution taking hold of the market until more specialized software comes in. Then again, only time will tell. Legaltech in action Legaltech, the way we define it, is concerned with applications that directly affect the practice of law and the provision of legal services. , a database of legaltech companies, comprises 9 categories. Those categories along with some examples are listed below. Stanford CodeX Techindex — Connect various market participants (lawyers, students, users) with each other Examples: , Marketplace Upcounsel Vakilsearch — Help customization and automation of various aspects related to legal documents Example: Document automation Legito — Help streamline processes, optimize workflows, and foster collaboration on an individual and team level Example: Practice management Clio — Organize legal information and make it accessible Examples: , , Legal Research Casetext Manupatra Indiankanoon — Provide legal classes or training to students or practitioners looking for continuous education opportunities Legal Education — Helps parties resolve small cases through online mediation, arbitration, or even without third-party intervention and hence, increase access to justice Examples: , Online Dispute Resolution Sama Presolv360 — Helps a legal party procure information in an electronic format from other relevant parties for purpose of legal proceedings E-discovery — Generate legal insights and predict outcomes from documents and data Example: Analytics Lex Machina — Monitor, track and ensure compliance with relevant regulations Example: Compliance Arachnys An about legaltech business models that I’d come across offers a more comprehensive categorization of legaltech companies. Below is an insightful chart of the same. independent report Not a conclusion There’s a need for stakeholder collaboration on multiple levels for legaltech to realize its promise. Judiciary should be provided with adequate infrastructure. Law practitioners and technologists should work together to build legaltech solutions. Legal education needs to prepare the upcoming workforce for industry developments. If venture capital infusion is any measure, legaltech has in the west. India, on the other hand, is only getting started. Startups in the space are few and far between. The state of tech and is still not as robust. Promising initiatives such as those run by not-for-profit and , India’s legaltech incubator, point to a positive future though. started to take off in recent years data in the judiciary Agami Prarambh The next decade should see technology permeate law in a meaningful way. The COVID pandemic will accelerate adoption. Here’s hoping that this development increases access to justice and improves the life of stakeholders. References and resources - a great resource for keeping up with the global legaltech ecosystem Artifical Lawyer blog - a great resource for those interested in the Indian legaltech ecosystem Agami Youtube channel - a great resource for keeping up with the Indian legaltech ecosystem Indian LegalTech blog Stanford Codex website Artificial Intelligence and Law – An Overview and History by Harry Surden at Stanford Law Business models in LegalTech companies - e-book The State of data in the judicial sector by Civic Data Lab AI in Law and Legal Practice – A Comprehensive View of 35 Current Applications If this topic interests you or if you have an interesting project that we can collaborate on, please feel free to reach out on Twitter , LinkedIn , or mail (akashtndn [at] gmail [dot] com). Previously published at https://www.akashtandon.in/technology/2020-06-16-legaltech-a-10000-feet-overview/

LegalTech - An Overview and Prospective Future

Intro: Grafana vs Kibana vs Knowi In today’s digital world when a person uses the term “Big Data”, the first thing which comes to mind is the sea of data that humans, social networks, and IoT devices are generating. But we often miss the big data deluge that is getting generated by servers, cloud infrastructure applications, and web-based business tools across the globe. Daily, these systems are generating tons of data in their logs. These log datasets are crucial to properly monitor these systems. But a person cannot monitor and analyze such huge amounts of log data in raw format manually. This is where data visualization tools can be a big help. Log data visualization dashboards can be useful for monitoring server and application logs to make an IT team more efficient. Grafana, Kibana. and Knowi are some of the best visualization tools available in the market--especially for . In this article, we shall give you a comparison of Grafana vs Kibana vs Knowi so that you can make the correct choice for your log management needs. Let us first understand each of them in more detail. Elasticsearch analytics Grafana Grafana is an open-source tool that can be used to visualize any data. It can be used to create multiple dashboards that support a variety of beautiful graphs and visualizations for different users and stakeholders. It has support for integration with a few different data sources, including Graphite, InfluxDB, and Elasticsearch. Grafana Dashboard (Source: Wikipedia.com) Kibana Kibana is the visualization tool of the very popular ELK stack. ELK stack is a collection of 3 open-sourced tools: Elasticsearch, Logstash, and Kibana. The ELK stack is used for end-to-end log management and monitoring solutions for Elasticsearch data. Kibana, which is the ‘K’ of ELK stack, is used for visualizing and analyzing the log data in Elasticsearch. Kibana does not support integration with any other data source and works only with Elasticsearch. Kibana Dashboard (Source: Elastic.co) Knowi Knowi is a modern business intelligence platform that has a host of features for both data engineers and data scientists. It has native integrations to plenty of SQL and NoSQL based data sources. Knowi provides rich visualizations for advanced analytics, on-demand reporting, and user dashboards. Keeping with the current disruption of artificial intelligence, Knowi also offers integration of machine learning and natural language processing. The ML feature helps users to build machine learning-based triggers in the workflow. While the feature allows non-technical users the ability to query the data just like they would ask questions in a google search. natural language business intelligence Knowi Dashboard from knowi.com Grafana vs Kibana vs Knowi Let us now do an in-depth comparison between these three tools on various parameters and understand which of the three will suit your requirements. Visualization Both Grafana and Kibana are essentially visualization tools and they offer a plethora of features to create graphs and dashboards. But Grafana is more popular for producing beautiful and visually appealing graphs and dashboards. Moreover, Grafana is known to be more customizable and flexible when compared to Kibana. But at the same time, Kibana is easier to set up. On the other hand, as a BI platform, Knowi also offers rich visualization. As a matter of fact, Knowi provides over 30+ visualizations while Kibana only supports 17. In spite of these differences, Grafana, Kibana, and Knowi are neck-to-neck in their visualization features. If you are not looking for something specific, then visualization should be least of your worries in deciding between Grafana, Kibana, and Kibana. A more important point to think about might be data source integration. Integration with Data Sources Grafana officially supports integration with 14 data sources like  MySQL, Graphite, InfluxDB, PostgreSQL, and Prometheus to name a few. Knowi, on the other hand, supports a wider range of 36 different data sources which include, Oracle, MySQL, MongoDB, PostgreSQL, Elasticsearch, etc. And those are just the listed native integrations. With its ability to connect to any REST API, the total number of possible data sources is limitless. The ability to tap data from multiple data sources makes both Grafana and Knowi truly versatile options for any sort of log management architecture. Kibana, however, only supports integration with Elasticsearch. It might look like a big shortcoming of Kibana, but on the positive side, if your project uses only Elasticsearch (and you do not think it will expand in the future to include more data sources) then you should consider Kibana as it is coupled tightly in the ELK stack. Grafana and Knowi, however, do still fully support Elasticsearch integration and can be considered over Kibana if they bring more on the table for your particular use-case. Log Search and Analysis While doing some analysis or debugging you may need to search through the logs. Both Knowi and  Kibana support querying on text data. Hence log analysis can be performed with these two tools. However, this support is not currently present in Grafana and is seen as a major drawback of choosing that platform. One use-case where this querying feature may be a good reason for you to choose either Knowi or Kibana over Grafana, is if you intend to build a monitoring solution for a technical support team that analyzes issues from logs. In such a use-case text-data querying would be essential. Proactive Alerts A crucial aspect of monitoring sensitive systems is to detect certain issues proactively so that action can be taken in advance before it leads to severe consequences. For example, we can set a rule to send alerts when a server memory utilization crosses 90%.  This will help the operations team to bring down the memory utilization and hence avoid any unwanted outage on the system. Both Grafana and Knowi have built-in features for raising alerts against a set of rules that can be defined by us. Kibana, however, does not offer support for Alerts out of the box. It instead requires 3rd party integration to enable Alerts. User Authorization In a big enterprise where there are many users and stakeholders, you would not like all users to access the data. This holds true even for monitoring dashboards. You would like certain users to access only certain dashboards. Or maybe your requirement is to create a dashboard specific to certain users. The good thing is that both Grafana and Knowi provide user management features out of the box. You can create users and roles, and authorize specific dashboard access to them. Kibana does not offer any user management features. This means whoever has the link to the Kibana dashboard can see the data. This might not always be a good way of working. But the saving grace is that we can still implement user and role restrictions in Kibana with the help of 3rd party integrations. Online Community Both Grafana and Kibana are open source and are under active development on GitHub. This means there is good online community support available for both if needed. If the popularity of both projects has to be measured, then it is worth noting that on Github, Kibana has more commits than Grafana. Knowi, however, is not an open-source tool currently. Still, they do have their own forum on their website where you can seek help from other users. But what they lack in community may be made up for in customer support.  As a relatively young startup looking to make a name for themselves, they have a dynamic, responsive, and strongly motivated team of solution engineers looking to prove that their product is the best on the market. Machine Learning Currently, machine learning is making inroads in all possible spaces globally where there is data. If you have loads of big data you would not like to miss the opportunity to apply machine learning to get hidden insights and patterns from data. Imagine, if you can predict any potential risk from logs data well in advance and take precautionary measures. This can take the log monitoring process to a whole new level. Unfortunately, neither Kibana nor Grafana support machine learning. But Knowi has built-in integration with many machine learning algorithms like Classification, Regression, and Time-Series Anomaly Detection type Machine Learning use cases, with clustering and deep learning coming soon. This enables Knowi users to do predictive analysis using machine learning and set up a triggering alert based on the prediction in the monitoring workflow. Purpose Till now, we have done a comparison of Grafana vs Kibana vs Knowi with respect to the features they offer. But we need to understand one thing that the underlying purpose of using the tools is slightly different. Kibana is useful to convert log data from the ELK stack into visualizations and it also supports querying logs. Grafana is more of a general-purpose visualization tool that can work with a handful of data sets that may or may not be “logs”. But it does not support text querying. Knowi is an end-to-end data analytics platform that can sit on top of and across multiple databases, structured or unstructured. Knowi literally supports all the features and integrations that either Kibana or Grafana offers plus much more. And it does have a big edge due to it’s out of the box machine learning solution. That said, both Kibana and Grafana can be deployed for free or at a very low cost, while Knowi is an enterprise solution that does have licensing fees. But with those added expenses come a lot of support, which can be nice when getting a complex data analytics solution off the ground. Conclusions People who work in the operation support team will tell you how important it is to monitor the health of applications, servers, and infrastructures. They continuously need to monitor these systems since an outage can result in a huge loss to the business. Grafana or Kibana or Knowi can be a good choice for your monitoring solution. As we saw, they have their own pros and cons, and differences in features. What will work best for you depends on your use case and you should evaluate your project needs before making the choice between the three platforms. About the Author Sean Knight has a M.S. in Data Science and a B.S. in Astrophysics. He has worked at particle accelerators, NASA JPL, and is now in the startup world as a growth hacker. Disclaimer: Sean works as Head of Growth at . Knowi Previously published at https://www.knowi.com/blog/grafana-vs-kibana/

Grafana Vs. Kibana Vs. Knowi: Battle Royale 2020

Stories, News & Mentions about CASETEXT

Casetext’s Stories on HackerNoon