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  "author": "Chip Huyen",
  "title_listed": "Machine Learning Tools Landscape v2 (+84 new tools)",
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  "publish_date": "2020-12",
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  "notes": "机器学习工具生态全景（v2）",
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  "title": "Machine Learning Tools Landscape v2 (+84 new tools)",
  "body_markdown": "[\nTwitter thread\n]\nLast June, I published the post\nWhat I learned from looking at 200 machine learning tools\n. The post got some attention and I got a lot of messages from people telling me about new tools. I updated the old list to now include 284 tools. I’ll keep on updating the list as I find out about new tools. Any lead would be much appreciated!\nWhile looking for these MLOps tools, I discovered some interesting points about the MLOps landscape:\nIncreasing focus on deployment\nThe Bay Area is still the epicenter of machine learning, but not the only hub\nMLOps infrastructures in the US and China are diverging\nMore interests in machine learning production from academia\nClick here to see\nthe list in Google Sheets\n. To see a fancy interactive chart, scroll to the end of this blog post.\n1. Increasing focus on deployment\nIn the list of 284 MLOps tools, there are 180 startups. Out of these 180 startups, 65 raised money in 2020. Most startups that raised money in 2020 are in still in the Data pipeline category, with an increasing number of in all-in-one (end-to-end platforms), hardware, and serving.\nHere are further breakdowns on the most popular categories:\nAccelerators (building chips optimized for machine learning algorithms, often with a focus on data centers)\nAI Apps platform (end-to-end platforms for developing & deploying AI applications)\nData management\nMonitoring\nEdge devices (building chips optimized for inference on consumer devices with low power)\n2. The Bay Area is still the epicenter of machine learning, but not the only hub\nAmong 65 MLOps startups that raised money in 2020, more than half of them are outside the Bay Area, with growing hubs in Boston, New York City, Israel.\n10 notable MLOps startups outside the Bay Area that raised money in 2020:\nDataRobot\n- Boston - $320M: enterprise AI apps platform\nGraphcore\n- UK - $250M: chips for machine learning\nDataiku\n- NYC - $100M: enterprise AI apps platform\nHailo\n- Israel - $60M: chips for machine learning\nDefinedCrowd\n- Seattle - $50.5M: training data generation\nZilliz\n- China - $43M: open-source software for processing unstructured data\nStarburst Data\n- Boston- $42M: distributed SQL query engine\nAnodot\n- Israel - $35M: enterprise data monitoring\nMaterialize\n- Boston -  $32M: stream processing\nRasa\n- Germany - $26M: API for conversational AI\nThis is an encouraging signals for people who want to build tools for machine learning production but don’t want to:\nsuccumb to the Bay Area monoculture and astronomical cost of living\ndeal with American fickle immgration policies.\nIt’s cheaper to find good engineers outside the Bay, and it might also be easier since you don’t have to compete with hundreds of tech giants and other startups for top engineers.\n3. MLOps infrastructures in the US and China are diverging\nOne term I saw while reading China-focused AI newsletters is “localization” (国产化替代) – replace foreign technologies with Chinese alternatives. Chinese companies use tools similar to what American companies use, but not quite the same.\nIn the US, companies rely on Google Cloud, Amazon AWS for hosting services. In China, companies rely on\nTencent Cloud, Alibaba Cloud\n. In the West, the Cloud Native Computing Foundation (CNCF) acts as the caretaker for container technologies including Kubernetes. In China, they have TARS Foundation for open-source microservices projects.\nOne reason for a separate foundation is language barriers. Chinese engineers might fork a popular repo, modify it for their use, then either can’t merge back because they don’t know how to communicate with the maintainers in English, or don’t want to merge back because it’d take a lot of time.\nThe list includes a few Chinese tooling startups, but it missed out so many because Chinese startups don’t get enough attention from the Western media. Many of them have their websites and documents only in Mandarin, which makes it hard to find information about them.\n4. More interests in machine learning production from academia\nThe AI research scene seemed to have calmed down in 2020 – Google freezed hiring for AI researchers,\nUber laid off their entire AI research team\n,\nElement AI was sold for cheap\n.\nHowever, the ML production scene is still growing with more and more tools on the market. There’s also a growing interest in ML production even from academia. Here are some of the venues for those who want to learn about ML production in academia:\nChallenges in Deploying and Monitoring Machine Learning Systems\nworkshop at ICML\nMLSys Seminars at Stanford\nConference on Machine Learning and Systems\nThe seemingly less steep curve from 2019 to 2020 is because a lot of companies that started this year are still in stealth.\nClick on a category to see the tools in that category. Click on the white space in the center to go up a level.\nAcknowledgment\n: Thanks\nLuke Metz\nfor being a faithful first reader.\nI want to devote a lot of my time to learning. I’m hoping to find a group of people with similar interests and learn together. Here are some of the topics that I want to learn:\nHow to bring machine learning to browsers\nOnline predictions and online learning for machine learning\nMLOps in general\nIf you want to learn any of the above topics, join our\nDiscord chat\n. We’ll be sharing learning resources and strategies. We might even host learning sessions and discussions if there’s interest. Serious learners only!",
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