MLOps Best Practices
Saket Kishore
Principal Data Scientist: Artificial Intelligence at UKG(Ultimate Kronos Group)| Architect | Gen AI | ex Deutsche Bank, IBM , Oracle
Every data scientist can relate to this quote. “…developing and deploying ML systems is relatively fast and cheap, but maintaining them over time is difficult and expensive.” – D. Sculley et al .
Perhaps you have encountered it in your search to solve a problem in one of the many moving parts of your machine learning system: data, model, or code.?
While it’s relatively easy to develop a model to achieve business objectives (item classification or predicting a continuous variable) and deploy it to production, operating that model in production comes with a myriad of issues.
Model performance may degrade in production for reasons such as data drift. You might need to change the preprocessing technique. This means new models need to be shipped into production constantly to address performance decline, or improve model fairness.
Hacking together a solution usually means incurring technical debt, which grows as your system ages and/or grows in complexity. Worse, you could lose time, waste compute resources and cause production issues. This calls for "MLOps".
Some practices you should definitely consider implementing are:
Try them out, and you’ll definitely see some improvement in your work on ML systems. What other factors/practices do you consider important ? I would love to hear your thoughts.
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1 年This is a great article that emphasizes the importance of data-centric MLOps throughout the ML lifecycle, which is crucial for successful machine learning projects. Learn more about data-centric MLOps here: https://aitech.studio/aie/mlops-best-practices/