Coursera - IBM AI-Native Data Engineering Professional CertificateReleased 7/2026
By Ruslan Podgaets, Antonio Cangiano et al.
MP4 |
Video: h264, 1280x720 |
Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning |
Language: English + subtitle |
Duration: 183 Lessons ( 10h 58m ) |
Size: 3.6 GB
⚡ Design AI-native data platforms for analytics, ML, semantic search, and RAG workflows.
⚡ Build vector retrieval systems, embedding pipelines, and governed lakehouse architectures.
⚡ Create reproducible ML-ready datasets and engineer unstructured data for AI use cases.
⚡ Apply CI/CD, observability, governance, and security practices to production AI data systems.
Skills you'll gain? Data Pipelines
? Test Data
? MLOps (Machine Learning Operations)
? Dependency Analysis
? Data Quality
? AI Enablement
? Solution Architecture
? Data Processing
? Extract, Transform, Load
? Enterprise Architecture
? Data Governance
? Feature Engineering
? Model Evaluation
? Software Documentation
? Dataflow
? Model Training
? Data Store
? AI Security
? Show all
Tools you'll learn? AI Workflows
? Operational Databases
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.
Applied Learning Project
Learners complete applied labs and portfolio-building work across the program, culminating in a capstone project where they design, build, validate, document, and operate an AI-native data platform. Projects include comparing traditional ETL pipelines with AI-native architectures, mapping AI workload requirements to platform components, designing semantic search and RAG workflows, engineering vector retrieval systems, preparing governed unstructured corpora, and creating reproducible ML-ready datasets. The capstone brings these skills together into a production-style system with architecture decisions, governance controls, observability plans, CI/CD support, and operational runbooks.
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