Course 03

Everything Data

The modern data stack for AI and ML - SQL and batch processing to streaming, vector databases, feature stores, and data pipelines for LLMs.

For ML engineers who need to build reliable data pipelines, feature stores, and training data workflows at scale.

The Modern Data Stack

Understand how modern data platforms are organized for analytics, machine learning, real-time systems, and AI applications.

Use SQL to create reliable datasets, features, labels, and diagnostics for machine learning workflows.

Design data models that support analytics, feature engineering, training, and serving without leakage or excessive complexity.

Build scalable batch data processing jobs for large training datasets and offline feature computation.

Design and implement streaming data systems for low-latency features, real-time analytics, and event-driven ML applications.

Use modern warehouses and lakehouse technologies to store, query, optimize, and govern data for AI systems.

Build reliable, observable, and maintainable data pipelines using modern workflow orchestration patterns.

Validate, monitor, and debug data and ML pipelines before bad data reaches models or users.

Build retrieval systems that transform documents into embeddings, store them in vector databases, and serve them to LLM applications.

Make datasets, features, experiments, and model outputs reproducible across time, teams, and environments.

Design, compute, store, serve, and monitor features for offline training and online inference.

Build data pipelines for pretraining, fine-tuning, evaluation, synthetic data generation, and human feedback loops for foundation models.

Every video is a public YouTube video. We pick the single clearest explanation for each lesson and credit the channel that made it.