Machine Learning Operations (MLOps)

In this one-day workshop, you will learn how to operationalize Machine Learning models using popular open-source tools (Kedro), and deploy it using cloud computing (Google Cloud Platform / Vertex AI).

During the course, we simulate real-world end-to-end scenarios of building a Machine Learning pipeline to train a model and deploy it in the cloud 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 Google Cloud Platform, leveraging managed and serverless services. All exercises will be done using either a local docker environment or GCP account.

   Target Audience

Data scientists and DevOps who are interested in implementing MLOps best practices, and build Machine Learning pipelines.


Some experience coding in Python, a basic understanding of cloud computing, and machine learning concepts

    Participant’s ROI

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

    Training Materials

All participants will get training materials in the form of PDF files containing slides with theory and an exercise manual with a detailed description of all exercises. During the workshops, the exercises can be done using either a local docker environment or within your IDE.

    Time Box

This is a one-day event (9:00-16:00), and there will be some breaks between sessions.


Session #1 - Introduction to Machine Learning Operations (MLOps)

  • Introduction and key concepts
  • MLOps components
  • The challenges of deploying and maintaining Machine Learning models in production
  • The Machine Learning model lifecycle

Session #2 - Kedro - a framework to structure your ML pipeline

  • Create reproducible, maintainable, and modular data science code
  • Build your Machine Learning pipeline
  • Hands-on exercises

Session #3 - Running Machine Learning pipelines in on the cloud environment

  • Introduction and key concepts
  • Example of Google Cloud Platform / Vertex AI Pipelines (serverless) deployment
  • Hands-on exercises

Session #4 - Building infrastructure for your Machine Learning platform

  • Overview of MLOps Frameworks landscape, and reference architectures
  • Write once - run everywhere: showcase of Kedro pipelines run on various clouds with Kedro plugins (Google Cloud Vertex AI, Amazon Sagemaker, Microsoft AzureML)

Session #5 - Summary and wrap-up

   Participants limit
18 participants

   Keywords: MLOps, Kubeflow, Kubeflow Pipelines, Kedro, Google Cloud Platform

    Session leader:

Machine Learning Engineer
GetInData | Part of Xebia
Senior MLOps Engineer



Evention sp. z o.o

Rondo ONZ 1 Str,

Warsaw, Poland


Weronika Warpas

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