Data for MLOps

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

Course #6 of MLOps Specialization · Coming Soon

ML models don’t fail because of bad code. They fail because of bad data — data that drifted, data that was split incorrectly, features that compute differently at training time and serving time. This course teaches you how to treat data with the same engineering discipline you apply to code: versioned, validated, monitored, and ready for production at every stage of the ML lifecycle.

  • Build reliable data ingestion pipelines from batch, streaming, and API sources
  • Version datasets and link data versions to model versions — so every model is reproducible
  • Engineer features correctly and serve them consistently — eliminating training-serving skew
  • Implement data validation gates that catch schema and statistical issues before they reach training
  • Prepare data for all model types — tabular, text, image, and LLM/GenAI workloads
Join Waitlist — Get Early Access Be first in line · Subscriber pricing at launch
🗄️
10 Content ModulesIngestion to LLM data
🛠️
10 Hands-on Labs10 Practical Quizzes
🏁
Capstone Projectwith Instructor Review
🏆
Certificate of CompletionStep 6 of the MLOps Specialization
📊
Tabular · Text · Image · LLMCourse #6 · Coming Soon

Sound familiar?

Your model was working fine. Then the data changed. Nobody noticed until the business did.

Most MLOps engineers learn how to get a model into production. Almost none are taught what to do once it’s there – how to monitor it, when to retrain it, how to replace it safely, and what to do when it starts failing. The lifecycle doesn’t end at deployment. It starts there.

In most ML teams, data is treated as a precondition — something the data science team handles before the pipeline starts. MLOps engineers are left to manage models without clear ownership of the data flowing through them. The result: silent failures, unexplained regressions, and production incidents that trace back to a data problem nobody was watching.

👻
“The model metrics look fine in staging but degrade in production within weeks.”
This is the classic data drift signature: the model trains on yesterday’s data distribution and serves tomorrow’s. Without drift monitoring, data version control, and clear retraining triggers, the degradation is invisible until it becomes a business problem.
🔀
“Our features compute differently during training and at serving time.”
Training-serving skew is one of the most common and hardest-to-diagnose ML production failures. A feature that aggregates over the last 7 days looks different when computed offline for training versus online for real-time inference. Without feature stores and point-in-time correctness, skew is almost guaranteed.
🗂️
“We can’t reproduce last quarter’s model because we don’t know which data it trained on.”
Data versioning is the foundation of ML reproducibility. Without it, every model training run is unrepeatable — you can’t roll back to a previous model version and be confident it behaves the same, because the data it saw no longer exists in a recoverable form.
This course was built to solve exactly that. Data for MLOps gives you the complete data management framework — from ingestion through validation, versioning, and drift monitoring — so the data flowing through your ML systems is as controlled, auditable, and reliable as your code.

What makes this different

Not a data engineering course.
Not a data science course.
Data management from the MLOps perspective.

Data engineering courses teach you to build data pipelines. Data science courses teach you to analyse data. This course teaches you to manage data the way MLOps engineers do — with versioning, validation gates, feature store consistency, and drift alerts built into the ML workflow.

✓

Covers the full data lifecycle for ML

From the first ingestion pipeline through validation, feature engineering, feature stores, dataset versioning, label management, and drift monitoring — the entire journey of data through an ML system, in the order that MLOps engineers encounter it.

✓

Feature stores taught as production infrastructure

Most courses explain what a feature store is. This course teaches you to build and use one — registering features, retrieving them correctly for both offline training and online serving, and avoiding the training-serving skew that makes otherwise good models fail silently.

✓

Data versioning linked to model versioning

The dataset versioning module doesn’t stop at “how to version a dataset” — it teaches you to link data versions to model versions in the registry, so every model you’ve ever trained is fully reproducible from both the code and the data it saw.

✓

10 focused hands-on labs — one per module

Each content module ends with a lab that applies the module’s concepts to a concrete task: build an ingestion pipeline, organize partitioned storage, version a dataset, build a feature store, implement validation checks. The labs span different data types, giving you breadth across tabular, text, image, and LLM data.

✓

LLM and GenAI data as a first-class module

Prompt/response data collection, cleaning, and filtering for LLM training data is a dedicated module — not a footnote. You’ll build a dataset of prompt-response pairs with the quality controls that responsible LLM fine-tuning requires.

✓

Labels, ground truth, and golden datasets

Label management is covered explicitly — label drift, labelling errors, and golden datasets for quality control. These are the data quality problems that cause model performance to degrade after launch, and they’re almost never covered in standard ML courses.

❌ Data engineering / data science courses

—Focus on Spark, Kafka, and large-scale processing
—Data quality is EDA, not a production validation gate
—No feature stores, no point-in-time correctness
—Labels and ground truth are assumed to be clean

✓ Data for MLOps

→Focuses on the data management layer of ML systems
→Data validation is a pipeline gate, not a one-time check
→Feature stores taught as serving infrastructure with skew prevention
→Label drift, errors, and golden datasets as production concerns

Learning outcomes

By the end of this course, you’ll be able to:

Six data management skills that give you full control over the data flowing through your ML systems – from the first byte ingested to the drift alert that triggers retraining.

🔌 Ingestion & Storage
✓
Build reliable ML data ingestion pipelines from batch, streaming, and API sources — and store data correctly for ML access patterns
Know which ingestion pattern to use for each data source, how to ensure reliability in the ingestion layer, and how to structure lakehouse storage with partitioning for efficient ML dataset access.
🗂️ Dataset Versioning
✓
Version datasets and link data versions to model versions — so every training run is reproducible and every model can be traced back to the exact data it saw
Implement dataset versioning with the patterns and tools used in production MLOps, and connect each data version to the corresponding model entry in the registry for full lineage.
⚡ Feature Engineering & Stores
✓
Engineer features reproducibly and serve them consistently — eliminating training-serving skew using offline and online feature stores
Build a feature transformation pipeline that produces the same results in batch training and in real-time serving, then register and retrieve those features from a feature store with point-in-time correctness.
✅ Data Validation
✓
Implement data validation gates — schema and statistical — that catch quality issues before they corrupt training runs or silently degrade production models
Write and automate the data validation checks that belong in every ML pipeline: schema conformance, statistical sanity (nulls, ranges, distributions), and contract tests for feature changes.
🏷️ Labels & Ground Truth
✓
Manage label data in production — detecting label drift, handling labelling errors, and maintaining golden datasets for ongoing quality control
Know the label management problems that cause model degradation after launch — label drift, systematic labelling errors, and distribution shift in ground truth — and the controls that keep them in check.
🤖 Multi-Modal & LLM Data
✓
Prepare data for tabular, text, image, and LLM/GenAI model types — applying the right preparation patterns and quality controls for each
Understand how data preparation differs across model types — and build a dataset of prompt-response pairs with the cleaning, filtering, and quality controls that responsible LLM training data requires.

Each outcome maps directly to a course module — see the full curriculum below.

View full curriculum ↓

What you’ll learn

Course Curriculum

10 Content Modules
10 Hands-on Labs
10 Quizzes
1 Capstone Project
#6 Specialization
🗺️01
Introduction 7 lessonsData lifecycle & roles
+
  • 📹 Course Introduction
  • 📹 About Your Instructor
  • 📹 Course Structure
  • 📹 What is Data in Machine Learning
  • 📹 Machine Learning Data Lifecycle
  • 📹 Roles and Responsibilities in Data Preparation
  • 📹 GitHub Repositories
🔌02
Data Collection & Ingestion 3 lessons🛠️ Lab · 📝 Quiz
+
  • 📹 Sources of ML Data — Batch, Streaming, APIs
  • 📹 Common Ingestion Patterns for ML
  • 📹 Ensuring Reliability in Data Ingestion
  • 🛠️ Build a Simple Ingestion Pipeline (Batch or Stream) — Hands-on Lab
  • 📝 Quiz — Data Collection & Ingestion
🗄️03
Data Storage 3 lessons🛠️ Lab · 📝 Quiz
+
  • 📹 Data Lakes, Warehouses, and Lakehouses for ML
  • 📹 File Formats for ML — CSV, Parquet, Avro, ORC
  • 📹 Partitioning and Layout for ML Data
  • 🛠️ Organize ML Dataset Storage with Partitions — Hands-on Lab
  • 📝 Quiz — Data Storage
🧹04
Data Preparation Basics 3 lessons🛠️ Lab · 📝 Quiz
+
  • 📹 Cleaning and Normalizing ML Data
  • 📹 Handling Missing and Imbalanced Data
  • 📹 Splitting Data for Training, Validation, and Testing
  • 🛠️ Prepare a Tabular Dataset for Model Training — Hands-on Lab
  • 📝 Quiz — Data Preparation Basics
🗂️05
Dataset Versioning 3 lessons🛠️ Lab · 📝 Quiz
+
  • 📹 Why Versioning is Critical in MLOps
  • 📹 Tools and Patterns for Data Versioning
  • 📹 Linking Data Versions with Model Versions
  • 🛠️ Version a Dataset and Connect It to a Model Registry — Hands-on Lab
  • 📝 Quiz — Dataset Versioning
⚙️06
Feature Engineering 3 lessons🛠️ Lab · 📝 Quiz
+
  • 📹 What is Feature Engineering
  • 📹 Feature Transformation in Batch vs. Serving
  • 📹 Avoiding Training–Serving Skew
  • 🛠️ Create a Reproducible Feature Transformation Pipeline — Hands-on Lab
  • 📝 Quiz — Feature Engineering
🏪07
Feature Stores 3 lessons🛠️ Lab · 📝 Quiz
+
  • 📹 What is a Feature Store
  • 📹 Offline vs. Online Feature Stores
  • 📹 Registering and Retrieving Features for ML Models
  • 🛠️ Build and Use a Simple Feature Store — Hands-on Lab
  • 📝 Quiz — Feature Stores
✅08
Data Validation & Quality Checks 3 lessons🛠️ Lab included
+
  • 📹 Common Data Quality Issues in ML Pipelines
  • 📹 Schema Validation for ML Data
  • 📹 Statistical Validation for ML Data
  • 🛠️ Implement Data Validation Checks in a Pipeline — Hands-on Lab
🏷️09
Labels & Ground Truth 3 lessons🛠️ Lab · 📝 Quiz
+
  • 📹 What is Label Data
  • 📹 Managing Label Drift and Errors
  • 📹 Golden Datasets for Quality Control
  • 🛠️ Create a Labeled Dataset with Quality Checks — Hands-on Lab
  • 📝 Quiz — Labels & Ground Truth
📊10
Data for Different ML Models 3 lessons🛠️ Lab · 📝 Quiz
+
  • 📹 Preparing Tabular Data for ML
  • 📹 Preparing Text Data for ML
  • 📹 Preparing Image Data for ML
  • 🛠️ Prepare a Dataset of Your Choice for Model Training — Hands-on Lab
  • 📝 Quiz — Data for Different ML Models
🤖11
Data for LLMs & GenAI 3 lessons🛠️ Lab · 📝 Quiz
+
  • 📹 Special Considerations for LLM Training Data
  • 📹 Prompt/Response Data Collection
  • 📹 Cleaning and Filtering for LLM Data
  • 🛠️ Build a Dataset of Prompt–Response Pairs — Hands-on Lab
  • 📝 Quiz — Data for LLMs & GenAI
🏁
Capstone Project with Instructor Review + Final Quiz
Apply the full data management framework to a real MLOps scenario — designing the data pipeline, validation gates, versioning strategy, and feature store approach for a complete ML system. Reviewed by Alex with personal written feedback.
Included

Is this course right for you?

Built for engineers who own ML systems — not just the models inside them

You don’t need to be a data scientist. You need to understand how data flows through ML systems, how it can go wrong silently, and how to build the management layer that keeps it reliable in production.

⚙️
MLOps & DevOps Engineers
Own the data layer, not just the pipeline
You manage ML pipelines but the data layer is someone else’s concern — until it breaks. This course gives you the vocabulary and tools to own data quality, versioning, and drift monitoring as first-class responsibilities in your operational role.
→ The validation, versioning, and drift monitoring modules apply directly to every data pipeline you operate or support.
🖥️
Platform & Data Engineers
Build data infrastructure that ML teams actually trust
You build data pipelines. This course teaches you what ML teams need from those pipelines that standard data engineering doesn’t account for: point-in-time correctness, feature store consistency, dataset versioning linked to model versions, and validation gates as automated pipeline steps.
→ The feature stores and dataset versioning modules give you the ML-specific context to make infrastructure decisions that prevent skew and enable reproducibility.
💻
Software Engineers supporting ML teams
Understand the data contracts your code must respect
You write the code that reads, transforms, and serves data. This course teaches you what makes ML data different from application data — schema contracts, point-in-time correctness, label quality — so the software you build handles data in ways that don’t silently break the models depending on it.
→ The data contracts, feature engineering, and validation modules directly inform how you design data APIs and transformation logic for ML systems.
📋
Technical Leads & ML Team Managers
Govern data quality as a team engineering standard
If you lead a team shipping ML models, data quality ownership is a governance problem as much as a technical one. This course gives you the framework — roles, validation gates, versioning policies, and drift alert criteria — to define clear data quality standards for your team.
→ The roles module, data lifecycle overview, and validation gate concepts map directly to the team standards and ownership models you need to define.

Prerequisites — what you need before starting:

✓ You need
✓ MLOps Fundamentals (Course #1) — field context
✓ Python for MLOps (Course #2) — or equivalent Python skills
✓ Basic understanding of ML concepts (training, inference, features)
✓ Familiarity with data files (CSV, JSON) and basic SQL or Pandas
✗ You do NOT need
✗ Data engineering experience (Spark, Kafka, Flink)
✗ Data science or statistics background
✗ Prior feature store or data versioning tool experience
✗ Experience with LLM training or fine-tuning

MLOps Specialization path

This is Course #1 of the full
MLOps Specialization

Each course builds on the previous one. Start here to get your field overview – then go as deep as your career requires.

1
Available now
MLOps Fundamentals
Full-field observability: AI/ML landscape, model lifecycle, CI/CD, infrastructure, monitoring, security, cost, and career paths – with real examples and a GitHub project.
Enroll → View course
2
Available now
Python for MLOps
Practical Python skills for MLOps contexts – scripting pipelines, working with APIs, handling data, and automating ML workflows. No data science prerequisites
Enroll → View course
3
Available now
Machine Learning Algorithms for MLOps
The most widely used ML algorithms explained for engineers who operate them, not develop them – behavior in production, resource footprint, and deployment characteristics for each model type.
Enroll → View course
4
Coming soon
Machine Learning Model Lifecycle
From experiment tracking to production monitoring – MLflow, model registry, serving patterns, drift detection, retraining, and rollback runbooks. 9 hands-on labs on the Customer Churn Prediction project.
🔔 Notify me
5
Coming soon
CI/CD for MLOps
Build ML pipelines from scratch: data CI, training CI, model packaging, container builds, registry promotion, deployment pipelines, safe rollouts, and LLM CI/CD – validated against 1k+ real job descriptions.
🔔 Notify me
6
▶ This course · Coming Soon
Data for MLOps Coming Soon
Data pipelines, quality contracts, versioning, lineage tracking, and feature stores – the data infrastructure layer that makes ML models reliable in production.
Join Waitlist – Early Access →
7
Coming soon
MLOps Observability & Monitoring
Production monitoring for ML systems: model performance dashboards, drift detection, alerting strategies, SLOs for ML services, and incident response for model failures.
🔔 Notify me
8
Coming soon
MLOps Security & Compliance
Supply chain security for ML systems, model governance, access control, compliance frameworks, and SecOps practices applied to ML infrastructure and pipelines.
🔔 Notify me
🎓
Specialization goal
MLOps Specialization Certificate
Nubes Academy is developing university accreditation partnerships. The specialization is designed to qualify as a recognized credential – not just another online certificate.

Start with MLOps Fundamentals and decide your next step based on your career goals.

Start with Course #1 – Enroll Now

Coming soon

Be first when Data for MLOps launches

Join the waitlist to secure subscriber pricing and get notified the moment the course goes live. Everything planned, confirmed from the curriculum draft:

  • 🗄️
    10 content modules — full data management stack From ingestion and storage through validation, versioning, feature stores, labels, and LLM data preparation.
  • 🛠️
    10 hands-on labs — one focused dataset task per module Build an ingestion pipeline, organize partitioned storage, create a feature store, implement validation checks, and more.
  • 📝
    10 quizzes + Final Quiz Reinforce understanding at the end of each content module.
  • 🏆
    Certificate of Completion Step 6 of the MLOps Specialization credential path.
Data for MLOps · Course #6
Price: coming soon
💡 Waitlist members receive subscriber pricing — a discount off the public launch price. No commitment required.
Join the Waitlist — Free
No commitment · Just first access and subscriber pricing
  • ✓ 10 content modules confirmed
  • ✓ 10 hands-on labs
  • ✓ 10 quizzes + final quiz
  • ✓ Capstone + instructor review
  • ✓ Certificate of Completion
Machine Learning Model Lifecycle — Certificate of Completion

Course credential

Certificate of Completion

Complete the course and earn a Certificate of Completion in Data for MLOps — shareable directly to your LinkedIn profile and CV. Data quality, versioning, and feature management are consistently cited as critical gaps in ML engineering roles at senior level, and a verifiable credential in this domain is a meaningful differentiator.

This certificate is Step 6 of the MLOps Specialization. Nubes Academy is developing university accreditation partnerships for the full specialization — the credentials you build today are designed to grow into something recognized beyond a standard online course certificate.

🎓 Part of the MLOps Specialization path Complete all courses in the specialization to qualify for the full Specialization Certificate — designed toward university accreditation.
Alex Pototskyy - Founder, Nubes Academy

Your instructor

About Your Instructor

Alex is an Independent Consultant with 20+ years of experience across Software development, DevOps, Architecture, and technical leadership. He developed and delivered Cloud & DevOps curriculum at Master’s Degree level at a university, and continues to work as an active practitioner alongside building Nubes Academy.

What makes Alex’s approach different: his courses are built from data, not assumptions. The MLOps Fundamentals curriculum was designed by analysing what MLOps engineers actually do in production, not by following a generic textbook outline. The result is a course that covers what your future employer expects, explained in a way that makes sense if you already think like an engineer.

If you have a background in IT, and you want to understand where ML fits into the systems you already work with – this course was built for exactly where you are.

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Got questions?

Frequently asked questions

The course is currently in development. Join the waitlist to be notified as soon as it’s available — waitlist members receive subscriber pricing at launch. We don’t publish a date until the course passes our quality review. Subscribe to the newsletter and you’ll hear first.
Neither. Data engineering teaches you to build large-scale data processing systems. Data science teaches you to analyse and model data. This course teaches you to manage data from the MLOps perspective — versioning, validation gates, feature store consistency, label quality, and drift monitoring. The tooling is simpler than data engineering; the focus is operational reliability, not analytical depth.
This course uses a different pedagogical approach: each of the 10 hands-on labs has a focused dataset task specific to that module’s topic — building an ingestion pipeline, organizing partitioned storage, versioning a dataset, building a feature store, and so on. The breadth-first approach means you practice each data management skill in its own relevant context, rather than applying all skills to one project. The final module lets you choose your own dataset type for the lab.
The course is intentionally tool-agnostic where possible, focusing on patterns and concepts that transfer across tools rather than locking you into a specific stack. That said, the labs use Python-based tools commonly found in MLOps environments — data validation libraries, dataset versioning patterns, and feature store concepts demonstrated with accessible tooling. Specific tool choices are still being confirmed as the course is developed. The model registry connection in the versioning lab links to MLflow, consistent with other courses in the specialization.
All three courses are complementary and cover different layers. Course #4 (ML Model Lifecycle) focuses on what happens to the model — tracking, packaging, registry, deployment, and drift. Course #5 (CI/CD for MLOps) focuses on automating the pipeline that moves models from development to production. This course (Course #6) focuses on the data that flows into and through both of those systems — how it’s ingested, stored, validated, versioned, and served. Understanding all three gives you end-to-end ownership of a production ML system.
Yes — data drift is covered across several modules. The Data Validation module covers statistical validation that detects distribution changes before training. The Labels module covers label drift as a specific quality control problem. The course overview mentions drift monitoring and retraining alerts as outcomes. The dedicated Observability course (Course #7) covers full drift dashboards and alerting infrastructure at scale — this course establishes the data management foundations that make that observability possible.
Yes — you’ll receive a Certificate of Completion in Data for MLOps shareable on LinkedIn and your CV. This is Step 6 of the MLOps Specialization. Nubes Academy is developing university accreditation partnerships — the full specialization certificate is designed to qualify as a recognized credential beyond a standard online course.
Yes. Team licenses for 5–20 engineers will be available, with a manager progress dashboard and kickoff session. This course is particularly valuable for platform and data engineering teams supporting ML — a shared vocabulary around data contracts, versioning policies, and validation gate standards significantly improves cross-team collaboration. Contact [email protected] to be included in early team pricing discussions.

Still have questions? Email us directly — we reply within 1–2 business days.

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

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