{"id":4127,"date":"2022-10-20T13:30:12","date_gmt":"2022-10-20T21:30:12","guid":{"rendered":"https:\/\/live-cometml.pantheonsite.io\/?p=4127"},"modified":"2025-04-24T17:17:04","modified_gmt":"2025-04-24T17:17:04","slug":"experiment-management-to-build-better-models","status":"publish","type":"post","link":"https:\/\/www.comet.com\/site\/blog\/experiment-management-to-build-better-models\/","title":{"rendered":"How Experiment Management Makes it Easier to Build Better Models Faster"},"content":{"rendered":"\n
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Sharing Best Practices Learned from the Best Machine Learning Teams in the World<\/p>\n

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Developing machine learning models can quickly become a messy, complicated process. As a hobbyist or learner working on a side passion project you often don\u2019t need to worry about tracking or recording your experiments, reproducing results, looking up previous model runs, or collaborating with others. You can get by perfectly fine with scratches of handwritten notes in your Moleskine or notes in the markdown cells of your Jupyter notebooks.<\/p>\n

These practices, however, won\u2019t cut it in an enterprise environment.<\/p><\/blockquote>\n

Whether you want to go pro, level up in your career, or start leading machine learning teams, you\u2019ll need to develop some good habits and understand some best practices.<\/p>\n

Developing machine learning models at scale for the enterprise is an iterative, experimental, collaborative process <\/strong>that can become messy and hard to manage.<\/p>\n

As the first data scientist in an enterprise, I\u2019ve experienced first hand the pain of not having guidelines and best practices in place for my model development process. I\u2019ve had to go back to the drawing board after the model I deployed started degrading in production. I\u2019ve had to traverse strings of code I wrote, thumb through (physical) notebooks, ctrl-f my way through text files, and go cell surfing through spreadsheets searching for a clues and trying to figure out what me from six months ago was thinking when building that model.<\/p>\n

I don\u2019t want you to go through that same headache.<\/strong><\/p>\n<\/div>\n

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\u201cIt\u2019s said that a wise person learns from his mistakes. A wiser one learns from others\u2019 mistakes. But the wisest person of all learns from others\u2019s successes.\u201d \u2014 J<\/strong>ohn C. Maxwell<\/strong><\/p><\/blockquote>\n<\/div>\n

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In this blog post, I\u2019ll share some lessons that will help you standardize your experimental process so you can quickly formulate, test, and evaluate hypotheses for your problem statement so that you can build better models, faster.<\/p>\n

A preview of what will be discussed in this post:<\/p>\n