Machine Learning Data

You can learn why data is core part of Machine Learning Operations

In this course, you will learn why data is a critical part of MLOps, and how to manage it to keep ML systems stable and ready for production. In real projects, models often fail not because of code, but because the data changes. MLOps solves this by treating data with the same care as software.

We will walk through how data flows across the ML lifecycle – from ingestion and validation to transformation, feature engineering, versioning, lineage, and drift monitoring. You’ll learn how MLOps Engineers build data pipelines that are reliable, testable, and ready to scale with lakehouse basics, safe splits, point-in-time rules, and simple PII handling.

By the end, you’ll know how to manage data in production every day, with clear steps for quality, governance, and reproducibility.

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What you’ll learn

✓ Understand how data flows through ML systems and how to build pipelines that are simple, clear, and repeatable.

✓ Use tools to version datasets, store features, and test data quality.

✓ Learn to clean, check, and track data to make sure your models work well and do not fail silently in production.

✓ Monitor how data changes over time, detect drift, and set up alerts so you know when retraining is needed.

Course Content

  • Course Introduction
  • About Your Instructor
  • Course Structure
  • What is Data for MLOps
  • GitHub repositories
  • Why Data is More Important Than Code in ML
  • What is the Role of Data in MLOps
  • Common Data Pitfalls in ML Projects
  • Overview of Data Workflow in a ML System
  • Types of Data Sources in ML Projects
  • Schema Management and Data Contracts
  • Data Lake, Warehouse, and Lakehouse: When to Use Each
  • File Formats for ML (CSV, Parquet, Avro, ORC)
  • Table Formats (Delta Lake, Iceberg, Hudi)
  • Partitioning, Compaction, and Retention Basics
  • Introduction to Data Ingestion for ML
  • Batch vs Streaming Ingestion in MLOps
  • Ingestion Architecture Patterns
  • Tools & Frameworks for Ingestion (e.g., Kafka, Spark, Debezium)
  • Why Data Validation Matters in MLOps
  • What to Validate: Schema, Range, Freshness, Completeness
  • Where Validation Runs: Ingestion, Feature, Pre-Train, Pre-Serve
  • Validating Datasets in Batch Pipelines
  • Validating Data in Streaming Pipelines
  • Automating Data Quality Checks
  • Data Quality Metrics & Reporting
  • Why Train/Validation/Test Splits Matter
  • Split Strategies (Random, Stratified, Time-based)
  • Data Leakage: Types and Real Examples
  • Point-in-Time Rules to Avoid Leakage
  • Practical Checks Before Training
  • What is Feature Engineering in MLOps
  • Common Transformations (Scaling, Encoding, Text, Time)
  • Online vs Offline Feature Pipelines
  • Managing Feature Consistency Across Environments
  • Introduction to Feature Stores
  • Feature Engineering Lifecycle & Governance
  • Best Practices & Anti-Patterns
  • Why Dataset Versioning Matters
  • What Does It Mean to Version a Dataset
  • Strategies and Tools
  • Reproducibility in ML Workflows
  • Connecting Datasets to the Model Lifecycle
  • Best Practices for Collaborative Versioning
  • What is Data Lineage and Why It Matters
  • Types of Lineage in MLOps
  • Capturing Metadata in ML Pipelines
  • Tags, Catalogs & Access Control
  • Scaling Lineage: What to Start With
  • What is a Data Pipeline in MLOps
  • Designing Modular ML Data Pipelines
  • Batch vs Streaming Pipelines in ML
  • CI/CD for Data Pipelines
  • Testing and Validating Data Pipelines
  • Monitoring and Observability for Data Pipelines
  • Building a Resilient Pipeline: Best Practices
  • What is Drift and Why It Kills Models
  • Types of Drift: Data vs Concept
  • Detecting Drift in Practice
  • Setting Thresholds and Alerting
  • From Detection to Action: Simple Playbooks
  • PII in ML Data: What MLOps Must Know
  • Masking, Tokenization, and Minimization Basics
  • Where to Apply PII Controls in Pipelines
  • Case Study: E-Commerce Recommender (Data Path)
  • Case Study: Predictive Maintenance for IoT (Sensor Data)
  • Failure Modes in ML Data Pipelines (Post-Mortems)
  • Design Patterns and Anti-Patterns

Start learning high demand tech skills today

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About Your Instructor

Hi, I’m Alex and I’ve spent over 20 years helping well known startups and enterprises introduce innovations. I also developed and taught Cloud&DevOps part for a Master’s Degree at the University.

In this course, I’ll show you what MLOps looks like in practice – step by step, with real tools and clear guidance.

You don’t need to be an expert. If you want to understand how to start or enforce your career as MLOps Engineer, not just in theory, but in real life, this course is for you. Let’s get started.

All Courses Access

  • Access to all current and future courses of Nubes Academy
  • Access to all reading materials and downloadable resources
  • GitHub repository with source code

Perfect for learners who want to master this topic by themselves.

Buy for 19,95 € / month

All Courses Access +
Capstone project + …

  • Access to all current and future courses of Nubes Academy
  • Access to all reading materials and downloadable resources
  • GitHub repository with source code
  • Quizzes
  • Capstone Projects and Expert review of your results
  • Certificate of course completion
  • Participation in live Q&A sessions with Experts
  • Participation in Slack community

The best option for serious learners who wanna master real-world skills with direct expert guidance.

Buy for 119,95 € / month

All courses are developed by experienced instructors with over 10 years of real-world industry expertise. We focus on delivering practical, up-to-date content – not just collecting enrollments, so that every course gives you real value.

Our courses meet high academic standards, and we’re actively working on certification to ensure they align with recognized best practices.

Each course includes video lectures, hands-on labs with screen recordings, quizzes, reading materials, GitHub repository with real project code, and a capstone project. This structure is designed to help you build practical, in-demand skills and knowledge that employers care about.

However, if you’re not satisfied for any reason, you can request a refund in accordance with our Refund Policy – your satisfaction matters to us.

It’s not just skills. It’s your next chapter.

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