MLOps 01 - What Is MLOps?
Why MLOps is necessary and what the real challenges of the ML lifecycle are
Posts tagged "mlops"
MLOps 02 - Data Pipelines and Feature Engineering
How raw data becomes training data, and why data quality matters more than model complexity
MLOps 03 - Experiment Tracking and Training Management
What goes wrong when experiments aren't tracked, and the tools that solve it
MLOps 04 - Model Versioning and Registry
Why model versioning differs from code versioning, and the role of a model registry
MLOps 05 - Model Serving and Deployment Strategies
How to serve trained models in production and deploy them safely
MLOps 06 - Monitoring and Drift Detection
Why production models degrade over time and how to detect it
MLOps 07 - CI/CD for ML
How ML CI/CD differs from software CI/CD and what needs to be tested and automated
MLOps 08 - Feature Stores
Why feature stores exist and how they solve the training-serving skew problem
MLOps 09 - GPU Infrastructure and Scaling
Why ML workloads demand specialized infrastructure and how to approach GPU scaling