Coursera — IBM AI-Native Data Engineering Professional Certificate 2026-7

Description

Study data-platform design for machine-learning and generative-AI applications through the curriculum identified as IBM AI-Native Data Engineering. The course connects structured and unstructured ingestion with data-lake architecture, embedding pipelines and vector retrieval. It also examines how to prepare reproducible datasets and map application requirements to the platform components that support them.

Operational sections address delivery pipelines, observability, governance and security for AI data systems. Practical work leads toward a platform project in which learners compare architectural choices, validate data flows and document how the system should operate. The emphasis is on the engineering behind an AI application's data supply, from initial ingestion to retrieval, monitoring and ongoing management.

Intended audience: Data engineers and developers moving into AI infrastructure. Technical professionals seeking practical AI data-platform design experience.

What you will learn

• Design data ingestion and storage arrangements for structured and unstructured AI inputs.
• Connect embedding pipelines and vector retrieval with governed data platforms.
• Prepare reproducible datasets for model-related workflows.
• Plan delivery automation, observability and security for operational data systems.
• Document a platform design against application and business requirements.

Course specifications

Publisher

Coursera

Instructors

Ruslan Podgaets, Antonio Cangiano

Language

English

Level

Beginner to Advanced

Duration

10 hours and 58 minutes

Lecture count

183

Subtitle

English

Quality

720p

Edition

2026-7

Course topics

View course topics

Prerequisites

• The source identifies SQL, Python, data pipelines, Git and basic software engineering as relevant background.

Introduction video

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Choose one edition and keep its parts together. Alternate providers are available when listed.

Size

2.1 GB

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