Best of MLOps — August 2025

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   Article

   Daily Dose of Data Science | Avi Chawla | Substack·41w

   The Full MLOps/LLMOps Blueprint

   MLOps extends beyond model training to encompass the entire production ML system lifecycle, including data pipelines, deployment, monitoring, and infrastructure management. The crash course covers foundational concepts like why MLOps matters, differences from traditional DevOps, and system-level concerns, followed by hands-on implementation of the complete ML workflow from training to API deployment. MLOps applies software engineering and DevOps practices to manage the complex infrastructure surrounding ML code, ensuring reliable delivery of ML-driven features at scale.

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2. 2
   Article

   Daily Dose of Data Science | Avi Chawla | Substack·40w

   The Full MLOps/LLMOps Blueprint

   A comprehensive crash course covering MLOps and LLMOps fundamentals, from foundational concepts to hands-on implementations. The series explores ML system lifecycle, data pipelines, model training, deployment, and monitoring. Part 3 focuses specifically on reproducibility and versioning using tools like Git, DVC, and MLflow, emphasizing that ML systems require extensive infrastructure beyond just the ML code itself.

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3. 3
   Article

   Daily Dose of Data Science | Avi Chawla | Substack·38w

   Data and Pipeline Engineering for ML Systems (With Implementation)

   A comprehensive MLOps crash course covering data and pipeline engineering for ML systems. The series explores data sources, ETL pipelines, model training, deployment, versioning, and reproducibility. It includes hands-on implementations using tools like PyTorch, MLflow, Git, DVC, and Weights & Biases, providing both foundational concepts and practical system-level thinking for production ML environments.

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