{"id":2312,"date":"2020-03-12T03:51:06","date_gmt":"2020-03-12T03:51:06","guid":{"rendered":"http:\/\/kusuaks7\/?p=1917"},"modified":"2024-01-01T14:24:30","modified_gmt":"2024-01-01T14:24:30","slug":"the-most-useful-ml-tools-2020","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/ai-ml\/the-most-useful-ml-tools-2020\/","title":{"rendered":"The Most Useful ML Tools 2020"},"content":{"rendered":"\t\t
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TD; DR<\/strong>\u00a0\u2014 Building good Machine Learning applications is like making Michelin-style dishes. Having a well organized and managed kitchen is critical, but there are too many options to choose from. In this article, I will highlight the tools I found useful in delivering professional projects, share a few thoughts and alternatives, and do a quick real-time survey (you can see what the community thinks after you participate).<\/p>\n\n<\/section>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Like any tooling discussion, the list is not exhaustive; but I try to focus on the most useful and simplest tools. Welcome any feedback in the comment section or let me know if there are better alternatives I should mention.<\/p>\n

Disclaimer<\/em><\/strong>: This post is not endorsed or sponsored. I use the term Data Science and ML interchangeably.<\/em><\/p>\n\n<\/section>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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A Common Question<\/h2><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\u201cHow do I build good Machine Learning applications?\u201d<\/p>\n

This question came up many times and in various forms during chats with aspiring data scientists in schools, professionals who are looking to switch, and team managers.<\/p>\n

There are many aspects of delivering a professional data science project. Like many others, I like to use the analogy of cooking in a kitchen: there is the ingredient (data), the recipe (design), the process of cooking (well, your unique approach), and finally, the actual kitchen (tools).<\/p>\n

So, this article walks through my\u00a0kitchen<\/strong>. It highlights the\u00a0most useful tools<\/strong>\u00a0to design, develop, and deploy\u00a0full-stack Machine Learning<\/strong>\u00a0applications \u2014 solutions that integrate with systems or serve human users in Production environments.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Overwhelming Possibilities<\/h2><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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We live in a golden age. If you search \u201cML tools\u201d in Google or ask a consultant, you are likely to get something like this:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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There are\u00a0(too) many tools<\/strong>\u00a0out there; the possible combination is infinite. It\u2019 can be confusing and overwhelming. So, let me help you to shrink it down. That said, there is no perfect setup. It all depends on your needs and constraints. So pick and choose accordingly.<\/p>\n

My list prioritizes the following (not in order):<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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