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My programming assignment, in general, is good, I got 81 out of 100, and I was planning to get more, but it is still I still want to work with your team, and looking forward to receiving good work from a great team. I received some notes about the PPT that it was very basic, but overall, I would really thank you for the quick response and actions from your side in a perfect timeline :)
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It is doable. However the main focus of MLFlow is in experiment tracking. I would suggest for you to look into another monitoring tools such evidentlyai . You can track more things than performance (e.g.data drift). Which may be helpful in a production setting. Source: about 4 years ago
Evidently is an open-source Python library that analyzes and monitors machine learning models. It generates interactive reports based on Panda DataFrames and CSV files for troubleshooting models and checking data integrity. These reports show model health, data drift, target drift, data integrity, feature analysis, and performance by segment. - Source: dev.to / over 4 years ago
Langfuse - Langfuse is an open-source LLM engineering platform that helps teams collaboratively debug, analyze, and iterate on their LLM applications.
LangSmith - Build and deploy LLM applications with confidence
Openlayer - Test, fix, and improve your ML models
Helicone AI - Open-source LLM Observability for Developers
Deepchecks Monitoring - Open Source Monitoring for AI & ML
Comet.com - Build better models faster