Phase 2: Data Engineering for Machine Learning

This phase dives deep into the critical processes of data engineering for machine learning. For ML specialists, the ability to efficiently and reliably collect, store, transform, and manage data is the bedrock of any successful ML solution. High-quality data engineering directly impacts model performance and the overall success of an ML project.

The First Principle is that effective data engineering for ML fundamentally establishes a robust and scalable data pipeline, ensuring data is ingested, stored, processed, and governed in a way that is fit for purpose, enabling accurate model training and reliable inference. This empowers ML specialists to build data-driven intelligent systems.

You will learn about various data ingestion methods, storage options, transformation techniques, and data governance practices, all using relevant AWS services.

The focus is on comprehending how to implement and design these data engineering patterns, which is crucial for the MLS-C01 exam.

Scenario: You need to build a data pipeline for an ML model. This involves ingesting data from various sources (databases, streaming events), storing it efficiently, transforming it for model training, and ensuring data quality and governance.

Reflection Question: How do robust data engineering principles and services fundamentally establish a scalable data pipeline, ensuring data is fit for purpose throughout its lifecycle, and enabling accurate model training and reliable inference?