Data Engineering for AI
Design data pipelines, lakes, and streaming systems that make machine learning and generative AI actually work in production.
Duration
8 weeks · 6-8 hours/week
Level
Intermediate
Delivery
Online
Status
Open for enrollment
Why this technology matters.
Data engineering for AI is the practice of building the pipelines, lakes, and streaming systems that feed machine learning and generative AI reliably in production. It matters now because models only perform as well as the data behind them — stale features, broken pipelines, and missing contracts silently degrade everything downstream.
It is used by data and ML teams to move batch and streaming data into lakes, feature stores, and retrieval indexes that serve training and inference. It solves freshness, reproducibility, and scale for AI workloads, but it does not solve bad modeling choices or unclear product decisions — a perfect pipeline carrying the wrong schema still produces the wrong behavior.
By the end you will be able to build a batch and streaming pipeline for AI workloads, a feature store with data contracts for model and retrieval teams, and a production-grade data lake setup that serves both training and inference.
Why this course exists
The gap is between a notebook trained on a static CSV and a production AI system fed by live, versioned, monitored data. The course teaches the arc from Sources to Pipelines to Models and feature stores to Decisions served reliably, so students learn to build the data foundation that makes ML and generative AI actually work.
Know exactly what you're signing up for.
Who is this for
Prerequisites
- Comfortable with Python and SQL
- Familiarity with cloud storage and APIs
- Basic data modeling knowledge
Technologies & tools
Skills you'll gain
A 8 weeks arc, module by module.
- Module 01
Week 1 — Data architecture for AI teams
- Module 02
Week 2 — Pipelines: batch, streaming, and contracts
- Module 03
Week 3 — Lakes, warehouses, and lakehouses
- Module 04
Week 4 — Data quality and observability
- Module 05
Week 5 — Feature stores and serving
- Module 06
Week 6 — Governance, lineage, and cost
- Module 07
Week 7 — Performance and scale
- Module 08
Week 8 — Capstone: a production-grade pipeline
Practical Training Flow
Learning → Guided Labs → Independent Practice → Industry Project → Capstone → Portfolio → Career Preparation. Practical hours are tracked alongside instructional hours and surfaced on the certificate.
Delivery as HIGAET Practical Training / Experiential Learning.
What you'll be able to do.
- Design batch and streaming pipelines for AI workloads.
- Implement data quality, governance, and lineage that survives reorgs.
- Build feature stores and data contracts for model and retrieval teams.
- Ship a production pipeline reviewed against operational rubrics.
You will build.
Every project ships as HIGAET Practical Training / Experiential Learning — portfolio-ready work, not exercises.
- Project 01
Batch pipeline for AI training data
- Project 02
Streaming ingestion pipeline with contracts
- Project 03
Feature store for model and retrieval teams
- Capstone
Production data platform serving ML and generative AI workloads
Speak the language first.
- Batch data pipelines
- Scheduled jobs that move and transform data in bulk, used for training sets and nightly refreshes.
- Streaming pipelines
- Continuously running flows that process events as they arrive for low-latency AI features.
- Data lakes
- Low-cost storage holding raw and processed data in open formats so many teams can reuse it.
- Feature stores
- Shared repositories that serve consistent, versioned input features to both training and live inference.
- Data contracts
- Written agreements on schema, freshness, and ownership between data producers and model or retrieval teams.
- Schema evolution
- Rules for changing data fields safely so downstream pipelines and models keep working.
- Data quality checks
- Automated validations for missing values, duplicates, and distribution shifts before data reaches models.
- Orchestration DAGs
- Directed graphs of pipeline steps with dependencies, retries, and schedules managed by an orchestrator.
- Embedding pipelines for AI
- Pipelines that chunk, embed, and index documents so retrieval systems always serve fresh content.
Fix, check, and go deeper.
Troubleshooting & common mistakes
Schema change breaks downstream models
Quarantine the offending batch with contract validation, pin the last good schema version, and require backward-compatible changes going forward.
Training-serving skew from mismatched features
Compare training and live feature values side by side, route both through the same feature store logic, and alert on divergence.
Streaming lag starves real-time features
Check consumer lag and partition throughput, scale consumers or repartition the topic, and backfill gaps from the raw log.
Duplicate events corrupt aggregates
Enforce idempotent writes with event IDs, deduplicate in the staging layer, and reconcile counts against source totals.
Silent data quality decay
Add freshness, null-rate, and distribution checks at ingestion, page the owning team on breach, and halt promotion of bad partitions.
Pipeline reruns are not reproducible
Pin code, data snapshot, and dependency versions per run, log run lineage, and rerun from the stored snapshot rather than live tables.
Before you move on, you should be able to
- Explain batch, streaming, and lake trade-offs for AI workloads
- Design pipelines that feed training, features, and retrieval reliably
- Build versioned datasets and feature stores for model teams
- Evaluate data quality and freshness with automated checks
- Diagnose skew, lag, and schema failures in production pipelines
- Deploy orchestrated pipelines with retries and lineage tracking
- Document data contracts shared with model and retrieval teams
Start your application.
Share a few details and a HIGAET advisor will reach out within one business day with next steps.
Common questions
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A 8 weeks course — Online Courses.