IBM AI-Native Data Engineering

IBM via Coursera Professional Certificate

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Modern AI systems rely on more than traditional ETL pipelines. In this advanced professional certificate, learners build the skills to design, validate, and operate production-grade AI-native data platforms that support machine learning, generative AI, semantic search, and retrieval-augmented generation. The program covers structured and unstructured ingestion, lakehouse architecture, embedding pipelines, vector retrieval systems, reproducible training datasets, CI/CD, governance, observability, and operational reliability. Designed for data engineers and adjacent technical professionals moving into AI platform work, this certificate helps learners move beyond isolated pipelines toward owning reliable, measurable, and governed AI data systems. Through hands-on labs and a portfolio-ready capstone, learners apply architecture and implementation skills to build an end-to-end AI-native data platform, document its tradeoffs, and demonstrate business value. To succeed, learners should already be comfortable with SQL, basic Python, data pipelines, Git, and core software engineering practices.

Syllabus

Course 1: Foundations of AI Native Data Engineering
Course 2: AI Systems, Automation, CI/CD and Data Engineering Workflows
Course 3: Vector Databases and Retrieval Data Engineering
Course 4: Lakehouse Architecture for AI-Native Data Platforms
Course 5: Unstructured Data Engineering for AI
Course 6: Reproducible Training Data and ML-Ready Data Pipelines
Course 7: Capstone: Build and Operate an AI-Native Data Platform

Courses

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This course focuses on the infrastructure awareness, automation practices, and engineering workflows needed to operate AI-native data systems. Learners work with Git-based project structure, CI/CD patterns, AI-assisted code and SQL generation, workflow automation, metadata generation, and policy-aware validation gates. The emphasis is on safe, reproducible engineering practices that support AI data products in production. By the end of the course, learners can manage code and data workflow changes with version control, implement CI/CD patterns, validate AI-generated artifacts, document data products, and deliver a minimal AI-native workflow with governance-aware automation. Topics include Git, repositories, CI runners, SQL/code generation, orchestration concepts, metadata, dataset cards, and validation checks.
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The capstone integrates all prior learning into a production-style portfolio project. Learners define a realistic AI-native data engineering use case, translate business and AI requirements into architecture decisions, ingest and model data, implement core platform components, and build at least one AI-native workflow such as vector retrieval, RAG, governed unstructured data preparation, or a reproducible training-data release. The project also requires governance, validation, documentation, and operations planning. By the end of the course, learners produce a portfolio-ready end-to-end AI-native data platform that demonstrates architecture, implementation tradeoffs, observability plans, CI/CD support, quality controls, access and governance decisions, and incident-response readiness. The capstone is designed to show applied, enterprise-relevant engineering judgment rather than only isolated technical tasks.
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This course introduces the foundational shift from traditional data engineering to AI-native data engineering. Learners reframe data platforms and pipelines as intelligent, production-grade assets that power analytics, machine learning, generative AI systems, semantic search, RAG workflows, and AI-powered assistants. This course explains how AI workloads consume data differently, introduces embeddings and vector retrieval as core primitives, demystifies LLMs as technical systems, and helps learners connect familiar data engineering skills to modern AI data lifecycles and AI-native architectures. The updated Course 1 design also emphasizes that production AI-native systems require ownership boundaries and collaboration across data engineering, ML/AI engineering, application engineering, platform engineering, security/governance, and product teams. By the end of the course, learners understand how modern AI systems are built end to end, how data engineering enables them, and how to redesign legacy pipelines to support semantic search, RAG, and AI-powered workflows.
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This course focuses on designing governed, scalable lakehouse architectures that support AI-native data platforms. Learners translate AI workload requirements into data product SLOs, compare open table formats, design ingestion and replay strategies, manage schema evolution, support reproducibility, and define observability signals for freshness, latency, throughput, and cost. The course emphasizes architecture and operational patterns rather than vendor-specific platform administration. By the end of the course, learners can explain when a lakehouse is preferable to a warehouse or data lake for AI workloads, compare Delta Lake, Iceberg, and Hudi at a practical level, design replay-safe ingestion patterns, and apply governance controls such as data contracts, lineage, access control, audit logging, and CI gates. The result is a production-oriented platform design for governed AI data systems.
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This course teaches learners to design reproducible, leakage-safe, governance-ready training data pipelines for machine learning and AI systems. Learners work with dataset versioning, deterministic builds, feature and label pipelines, point-in-time correctness, slice validation, drift monitoring, and CI-based release gates. The course treats training datasets as governed data products with owners, readiness criteria, quality expectations, and reproducibility requirements. By the end of the course, learners can produce reproducible dataset releases, identify temporal and target leakage risks, design feature and label workflows, track changes across versions, and apply release gates for schema, distribution, slice, bias, and reproducibility checks. The focus is on operationally reliable training data pipelines that teams can trust in production AI development.
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This course prepares learners to engineer governed unstructured data pipelines for AI training, semantic search, and RAG systems. Learners design ingestion workflows for documents and multimodal content, apply extraction and OCR-aware processing, normalize text while preserving useful structure, detect sensitive information, create chunking strategies, and enrich corpora with metadata for citation, filtering, lineage, and governance. By the end of the course, learners can build or specify unstructured ingestion workflows, evaluate extraction quality, prepare corpora for embedding or training, and apply safety and quality gates for PII, bias, source trust, and licensing risk. The course emphasizes corpus quality and governance so learners can produce reliable data assets for downstream AI systems.
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This course teaches learners to design, operate, secure, and evaluate vector-based retrieval systems used in semantic search and RAG applications. Learners work with embeddings, vector schemas, index design, refresh strategies, consistency checks, evaluation signals, retrieval observability, and permissions-aware access controls. The course focuses on practical system design and tradeoffs rather than low-level algorithm implementation. By the end of the course, learners can explain how embeddings enable semantic retrieval, compare dense, sparse, and hybrid retrieval patterns, design vector database schemas and refresh workflows, evaluate retrieval quality, and apply governance and security controls to retrieval systems. Topics include vector stores, metadata filtering, HNSW and IVF concepts, recall/latency tradeoffs, retrieval drift, and audit logging.