From Notebooks to Production #7: Ensuring Data and Model Quality while Addressing Model Drift in Data Science Projects
From Notebooks to Production #6: Unit & Integration Testing for Data Science Projects with Pytest
From Notebooks to Production #5: Building for Tomorrow - Maintaining Data Science Projects with Cross-Project Python Packages for Seamless Scalability
From Notebooks to Production #4: Streamlining Model Logging, Version Control, and Experiment Tracking with MLflow in Data Science Projects
From Notebooks to Production #3: Streamlining Python Module Packaging with Wheels and Makefile Automation in Data Science Projects
From Notebooks to Production #2: Managing Data Science Project Dependencies and Configurations, Enabling Project Portability
From Notebooks to Production #1: 6 Ways to Level Up Your Python Code for Data Science
How to Monitor Data Science Projects in Production Using Slack Channel Alerts, Making MLOps Easy