Week 3 - MLOps Engineering on AI Platform Engineering Handbook

ML Lifecycle & Experiment Tracking

Mon, 01 Jan 0001 00:00:00 +0000

1. Machine Learning Lifecycle Overview

Before diving into individual concepts, you need a mental model of the complete journey a machine learning project takes from idea to production. Most engineers new to ML think the lifecycle is: get data, train model, done. In reality, that’s about 10% of the work. The full lifecycle is a continuous loop involving many disciplines, many failure modes, and many handoffs between people and systems.

Model Packaging & Serving

Mon, 01 Jan 0001 00:00:00 +0000

1. Model Serialization Techniques

You’ve trained a model. The training process ran for hours, consumed gigabytes of GPU memory, and produced a set of learned parameters — numbers representing the patterns the model discovered in your data. When training finishes, those parameters exist only in your process’s memory. The moment that process ends, they’re gone forever unless you save them to disk. Serialization is the process of converting those in-memory parameters and model structure into a format that can be stored persistently and loaded back later.

CI/CD for Machine Learning

Mon, 01 Jan 0001 00:00:00 +0000

1. ML Pipeline Architecture

Before getting into individual concepts, you need a mental model of what an ML pipeline actually is and why it needs to be a pipeline at all rather than a collection of scripts someone runs manually.

Data Validation, LLM Ops & Monitoring

Mon, 01 Jan 0001 00:00:00 +0000

1. Data Quality Validation Concepts

Let’s start with the most important insight about data quality in machine learning: bad data is the silent killer of ML systems. Unlike application bugs that crash loudly and obviously, bad data produces models that fail quietly. The model keeps running, keeps returning predictions, and nobody realizes anything is wrong until the damage is done — wrong loan approvals, missed fraud, bad recommendations, or incorrect medical predictions that affected real patients.