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Data Scientists, ML Engineers
e.g. ML, MLOps, Python, Kedro, Kubeflow, Kubernetes, Terraform, Docker
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Machine Learning Operations Training (MLOps)

This four-day course will teach you how to operationalize Machine Learning models using popular open-source tools, like Kedro and Kubeflow, and deploy it using cloud computing.

During the course we will simulate real-world end-to-end scenarios – building a Machine Learning pipeline to train a model and deploy it on a Kubeflow environment. We’ll walk through the practical use cases of MLOps for creating reproducible, scalable and modular data science code. Next, we’ll propose a solution for running pipelines on a cloud (GCP, AWS or Azure), leveraging managed and serverless services. All exercises will be performed using either a local docker environment, or cloud account (GCP, AWS or Azure).

The scope of the course can be extended or customized upon request to cover specific machine learning topics or managed cloud solutions.

Training outcome

After the training participants will get:

  • Practical knowledge of building Machine Learning pipelines using Kedro
  • Hands-on experience with building Machine Learning platform with Kubeflow Pipelines
  • Tips on real world applications and best practices

Course agenda*

Day 1

Machine Learning and ML Ops fundamentals

  • Introduction to Machine Learning Operations (MLOps)

  • Introduction and key concepts

  • MLOps components

  • Challenges of deploying and maintaining Machine Learning models in production

  • The Machine Learning model lifecycle

Day 2

Structuring ML project with Kedro

  • Kedro - a framework to structure your ML pipeline

  • Create reproducible, maintainable and modular data science code

  • Build your Machine Learning pipeline

  • Hands-on exercises

Day 3

Developing and orchestrating ML pipelines with Kubeflow

  • Kubeflow and Kubeflow Pipelines

  • Introduction and key concepts

  • Example of Kubeflow Pipelines (managed) and serverless pipeline deployments (Vertex AI, Sagemaker)

  • Hands-on exercises

Day 4

Building MLOps infrastructure

  • Building infrastructure for your Machine Learning platform

  • Overview of MLOps Frameworks landscape, and reference architectures

  • Summary and wrap-up

* GetInData reserves the right to make any changes and adjustments to the presented agenda.


Our workshops and training programmes are prepared and conducted by experienced instructors with many years of real-life Big Data Analytics experience. Get to know our team!

More Information

GetInData reserves the right to introduce changes to the training agenda. All of the hands-on exercises are performed in a local or cloud (Google Cloud, AWS or Microsoft Azure) environment. For the sake of doing exercises, each participant should have their own laptop with a web browser and terminal. Training materials (presentations and lab instructions) will be made available to all participants in PDF format. Training can be scheduled in the venue and date agreed by GetInData and client.

Contact person

Klaudia Wachnio
Piotr Krewski
+48 888 185 137


Completed in half the estimated time and with a fivefold improvement on data collection goals, the robust product has exponentially increased processing capabilities. GetInData’s in-depth engagement, reliability, and broad industry knowledge enabled seamless project execution and implementation.

Wojciech Ptak

GetInData had been supporting us in building production Big Data infrastructure and implementing real-time applications that process large streams of data. In light of our successful cooperation with GetInData, their unique experience and the quality of work delivered, we recommend the company as a Big Data vendor.

Miłosz Balus

GetInData delivered a robust mechanism that met our requirements. Their involvement allowed us to add a feature to our product, despite not having the required developer capacity in-house.

Stephan Ewen

Their consistent communication and responsiveness enabled GetInData to drive the project forward. They possess comprehensive knowledge of the relevant technologies and have an intuitive understanding of business needs and requirements. Customers can expect a partner that is open to feedback.

Wilson Yu Cao
Development Team Manager

We sincerely recommend GetInData as a Big Data training provider! The trainer is a very experienced practitioner and he gave us a lot of tips regarding production deployments, possible issues as well as good practices that are invaluable for a Hadoop administrator.

Mariusz Popko
Platform Manager

The engineers and administrators at GetInData are world-class experts. They have proven experience in many open-source technologies such as Hadoop, Spark, Kafka and Flink for implementing batch and real-time pipelines.

Kostas Tzoumas

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