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Career route 3

AI Data Systems

For roles like AI Data Engineer, Machine Learning Data Engineer, Retrieval Engineer

Build reliable data, feature, embedding, retrieval, evaluation, and observability pipelines for AI systems.

11 lessons about 15 h of lessons Ends in a certificate Paid plans
Lesson checks passed0 of 11
Start: Data Management

Is this route for you?

Do you want to make the data behind training, retrieval, evaluation, and production behavior trustworthy?

  • You care about lineage, repeatability, and data quality more than demos.
  • You enjoy tracing model behavior back to data and retrieval decisions.
  • You want to connect data systems with measurable AI outcomes.

What you'll be able to do: Build on shared foundations to create traceable AI data systems that transform source data into measurable model and retrieval inputs.

Start with

  • Complete the shared software engineering and AI foundations, or demonstrate equivalent working knowledge.
  • Be comfortable with tabular data, features, train and test splits, and basic evaluation.
  • Be able to inspect files, schemas, and pipeline output from code.

The route

Common CoreBuild reproducible data, feature, and language-model preparation pipelines.
  1. 1Data ManagementPhase 0: Setup and tooling · 45 min
  2. 2Feature Engineering & SelectionPhase 2: Machine learning fundamentals · 1.5 h
  3. 3ML PipelinesPhase 2: Machine learning fundamentals · 2 h
  4. 4Data Pipelines for Pre-TrainingPhase 10: LLMs from scratch · 1.5 h

You produce: A versioned pipeline with deterministic transforms, split checks, and a reproducible run record.

Role PracticeBuild context and retrieval paths whose quality can be measured independently.
  1. 5Embeddings & Vector RepresentationsPhase 11: LLM engineering and RAG · 1.5 h
  2. 6Context Engineering: Windows, Budgets, Memory, and RetrievalPhase 11: LLM engineering and RAG · 1.5 h
  3. 7RAG (Retrieval-Augmented Generation)Phase 11: LLM engineering and RAG · 1.5 h
  4. 8Advanced RAG (Chunking, Reranking, Hybrid Search)Phase 11: LLM engineering and RAG · 1.5 h

You produce: A retrieval system with a documented corpus, index configuration, and retrieval metrics.

Proof ProjectConnect the data path to an evaluated production-style application.
  1. 9Evaluation & Testing LLM ApplicationsPhase 11: LLM engineering and RAG · 45 min
  2. 10Building a Production LLM ApplicationPhase 11: LLM engineering and RAG · 2 h

You produce: A traceable AI data pipeline with end-to-end quality checks and application-level evaluation.

Interview and Readiness EvidenceShow how telemetry separates data, retrieval, and generation failures.
  1. 11LLM Observability Stack SelectionPhase 17: Infrastructure and production · 1 h

You produce: A failure analysis that links a production symptom to the responsible data or retrieval stage.

Portfolio project: Traceable AI Data Pipeline

Build a reproducible data and retrieval path whose inputs, transformations, quality checks, and downstream effects can be inspected.

  • A reproducible ingestion and transformation pipeline
  • Leakage, schema, and data quality checks
  • Embedding and retrieval evaluation results
  • Observability for data-dependent failures
  • A provenance and rollback note

You're ready when you can

  • Explain lineage from raw data to model or retrieval input.
  • Prevent leakage and verify feature transformations.
  • Measure retrieval quality separately from generation quality.
  • Identify a data, embedding, retrieval, or model cause from evidence.
  • Reproduce a pipeline result and document how to roll it back.

What this route covers, honestly

Covered in depth
  • Data and feature pipelines
  • Embeddings and retrieval
  • RAG evaluation
  • AI observability
Partly covered
  • Lineage and schema evolution
  • Pipeline orchestration and backfills
  • Data governance
Outside the course
  • Enterprise warehouse or lake ownership
  • Compliance authority
  • Live data platform on-call experience

AI Data Systems career route certificate

Pass all 11 lesson checks on this route (0 done), then the 20-question route exam drawn from these lessons. The certificate lists every topic you covered and carries an ID anyone can verify.

Exam requirements

Completing this route shows that you can build and evaluate a traceable AI data pipeline. It does not guarantee a role or establish experience operating an enterprise data platform. This is a specialist overlay after shared foundations. It samples training, feature, and retrieval data work, but does not claim mastery of every warehouse, lake, streaming, or governance platform. The estimate covers lesson time only. Portfolio work and practice take additional time.