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Linux Internals 10 - Containers and Virtualization
How namespaces and cgroups create containers, and a wrap-up connecting all kernel concepts
Lectures
All posts in the Lectures
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MLOps 01 - What Is MLOps?
Why MLOps is necessary and what the real challenges of the ML lifecycle are
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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