Live online course
Data stack for MLOps, ML Platforms and ML Pipelines
Data management and machine learning pipelines are two crucial components of data science. They are key to unlocking insights from data and building robust machine learning models that can make accurate predictions.
  • 6 live sessions start coming soon
  • 20 learners per course
  • early birds €800 (full price €1 200) per seat.
    Use your L&D budget to cover the cost!
  • language: English
Designed for ML Engineers, data scientists, data engineers, DevOps, and software engineers
Gain new skills:
  • learn how to store, manage, and version data and features
  • get hands-on experience with building data and ML pipelines
  • deploy and utilize the end2end ML platform - Kubeflow
About live online course by Dmytro Voitekh

Now, that you are familiar with the spectrum of problems addressed by MLOps and laid the foundations of the ML tooling for deployment, it’s time to master data management and ML pipelines.


We will explore both standalone tools and ML platforms that you can easily deploy and use in your projects to standardize and automate your workflows.

Session 1

How to deal with data

Data-driven vs data-centric
Types of data storage used for ML
Data management and versioning for ML projects
Tools and frameworks
Session 2

Data quality and Feature Stores

How to ensure data quality: drift detection, outliers, data profiles, etc
Tools for data quality and monitoring
Why do we need Feature Stores
Feast and alternatives
Session 3

Spark for ML pipelines

Spark concepts and architecture
PySpark, dataframe, UDF, and many other handy Spark features
How to build data and ML inference pipelines in Spark
Does it make sense to train models in Spark
Session 4

Kubeflow ecosystem

End2End ML platforms overview
Kubeflow architecture. Argo Workflows
How to deploy Kubeflow
Kubeflow features: notebooks, tensorboards, AutoML, and pipelines
Kubeflow alternatives
Session 5

Kubeflow Pipelines (KFP)

DAGs and KFP concepts
Ways to define Kubeflow Pipelines, DSL
KFP artifacts: metrics, visualizations, files, and logs
How to enable CI/CD for your pipelines
Kubeflow UI vs SDK
Session 6

Demo day

Present team projects
Retrospective and follow-up questions

Meet the top expert leading your course

Lead ML Engineer

Dmytro Voitekh is a seasoned ML engineer with 8 years of experience in machine learning, MLOps, and full-stack engineering. He has a strong background in helping both early-stage startups and established tech companies to leverage ML features. His domains of expertise include machine learning and MLOps, and he has worked as an ML consultant, ML architect/lead, and CTO of a startup.


Dmytro has worked with a number of companies, including Proxet and GIPHY, and his extensive experience in the field makes him a valuable asset for any company that wants to incorporate machine learning into their business processes. He has numerous talks, workshops, and practical courses dedicated to ML, which showcases his expertise in the field.

Use your L&D budget for upskilling

Send us a 1-click request to get all the details of corporate learning at Data stack for MLOps, ML Platforms and ML Pipelines

Come for the experts — stay for the community

We unite a multi-faceted European AI ecosystem into one.


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