MLOps 01 - What Is MLOps?
Why MLOps is necessary and what the real challenges of the ML lifecycle are
MLOps Fundamentals
Building reliable ML systems from data pipelines to production monitoring
ML engineers, data scientists, and backend engineers moving from model experiments to production operations.
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MLOps 02 - Data Pipelines and Feature Engineering
How raw data becomes training data, and why data quality matters more than model complexity
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MLOps 03 - Experiment Tracking and Training Management
What goes wrong when experiments aren't tracked, and the tools that solve it
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MLOps 04 - Model Versioning and Registry
Why model versioning differs from code versioning, and the role of a model registry
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MLOps 05 - Model Serving and Deployment Strategies
How to serve trained models in production and deploy them safely
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MLOps 06 - Monitoring and Drift Detection
Why production models degrade over time and how to detect it
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MLOps 07 - CI/CD for ML
How ML CI/CD differs from software CI/CD and what needs to be tested and automated
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MLOps 08 - Feature Stores
Why feature stores exist and how they solve the training-serving skew problem
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MLOps 09 - GPU Infrastructure and Scaling
Why ML workloads demand specialized infrastructure and how to approach GPU scaling
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MLOps 10 - Building an MLOps Platform
How to bring all MLOps components together into a unified platform