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Reproducible Training Data and ML-Ready Data Pipelines

IBM via Coursera

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Details

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

9 weeks, 2 hours a week
Sessions

Self-Paced
Level

Intermediate
Subtitles

English

Found in

Part of

IBM AI-Native Data Engineering

Overview

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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.

Syllabus

Welcome to the Course
- This welcome module introduces Course 6 and explains why reproducible, governed, ML-ready training data is essential for trustworthy AI and ML work. Learners get a high-level view of the course goals, recommended prerequisites, and the learning journey ahead before starting the technical modules.
Training Data as a Product
- This module teaches learners to treat training data as a governed data product with explicit ownership, contracts, service expectations, documentation, and release-readiness checks. Through applied labs, learners create core artifacts such as a product brief, dataset contract, acceptance criteria, dataset card draft, and readiness checklist for an ML-ready training dataset release.
Versioning and Reproducibility
- This module teaches learners how to make training datasets reproducible, auditable, and release ready through versioning, snapshots, hashing, deterministic builds, lineage, and reconstruction evidence. Learners create practical release artifacts that help teams identify exactly what data trained a model, explain what changed, and rebuild a dataset release with confidence.
Leakage and Contamination Controls
- This module teaches learners how to detect and control leakage and contamination risks that can invalidate model evaluation and undermine reproducible training data releases. Learners practice identifying temporal, target, cross-split, and semantic risks, validating split integrity and point-in-time correctness, and documenting mitigation and escalation decisions as release evidence.
Feature Engineering Pipelines
- This module teaches learners to design feature engineering pipelines that produce ML-ready data with reproducibility, freshness, quality, lineage, and training/inference consistency. Learners create practical artifacts such as a feature pipeline specification, freshness rules, quality checks, and a drift monitoring plan to support trustworthy dataset releases.
Label Pipelines and Ground Truth Management
- This module teaches learners how to design governed label pipelines and ground truth workflows for supervised ML, including source selection, taxonomy design, review policies, quality checks, drift monitoring, and versioning. Learners produce project-ready artifacts and validation evidence that make labels auditable, reproducible, and ready for release decisions.
CI Validation for AI-Grade Data Quality
- This module teaches learners to implement CI-style validation gates that verify AI-grade training data quality before release. Learners build repeatable checks for schema, distributions, slices, bias-related risks, leakage, reproducibility, and release readiness, then package the resulting evidence into a governed final dataset release package.
Final Exam
- This Final Exam assesses your ability to apply the course’s release oriented approach to reproducible training data and ML ready data pipelines. You will complete a quiz focused on evidence backed release decisions and a case study that tests your judgment on governance, leakage, reproducibility, and release controls in realistic scenarios.
Course Summary
- This closing module wraps up Course 6 by helping you reflect on what you have accomplished and reinforce the course's core themes around reproducibility, governance, and ML ready data quality. It also previews how these skills carry forward into Course 7, where you will apply them in an integrated capstone experience.