Data and Pipeline Engineering for ML Systems (With Implementation)
The full MLOps/LLMOps blueprint.
Part 6 of the MLOps and LLMOps crash course is now available, which continues with building scalable data pipelines in ML systems we covered in Part 5.
Read here: MLOps and LLMOps crash course Part 6 →
Data pipelines form the structural backbone that supports the implementation of all subsequent stages in the MLOps lifecycle.
Thus, we cover:
How to sample data for machine learning tasks
Pitfall of data leakage and how to avoid it.
Feature stores
And then a practical deep dive into building an end-to-end feature pipeline.
Just like all our past series on MCP, RAG, and AI Agents, this series is both foundational and implementation-heavy, walking you through everything that a real-world ML system entails:
In Part 1, we covered the foundations:
Why does MLOps matter?
MLOps vs. DevOps and traditional software systems
System-level concerns in production ML
The ML system lifecycle.
In Part 2, we went hands-on and covered:
The entire ML system lifecycle.
Data pipelines
Model training and experimentation
Model deployment and inference
Model deployment and inference
Hands-on project from training to API
In Part 3, we covered reproducibility and versioning for ML systems:
Why reproducibility matters and challenges.
9 industry best practices for reproducibility and versioning.
PyTorch model training loop and model persistence.
Git + DVC for version control.
Training and tracking experiments with MLflow.
In Part 4, keeping W&B central to the implementations, we cover:
Experiment tracking.
Dataset and model versioning.
Reproducible pipelines.
Model registry.
In Part 5, we started data and pipeline engineering, as viewed from a systems perspective, explaining:
Data sources and formats
ETL pipelines
Practical implementation
Only a tiny fraction of an “ML system” is the ML code; the vast surrounding infrastructure (for data, configuration, automation, serving, monitoring, etc.) is much larger and more complex:
We are creating this MLOps and LLMOps crash course to provide a thorough explanation and systems-level thinking to build AI models for production settings.
Just like the MCP crash course, each chapter will clearly explain necessary concepts, provide examples, diagrams, and implementations.
Thanks for reading!