DataMass Gdańsk Summit 2022
phone: +48 570 272 723

Machine Learning Operations (MLOps)

In this one-day workshop, you will learn 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 simulate real-world end-to-end scenarios – building a Machine Learning pipeline to train a model and deploy it in 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 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 building 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 - Kubeflow and Kubeflow Pipelines

  • Introduction and key concepts
  • Example of Kubeflow Pipelines (managed) and Vertex AI (serverless) deployments
  • Hands-on exercises

Session #4 - Building infrastructure for your Machine Learning platform

  • Overview of MLOps Frameworks landscape, and reference architectures

Session #5 - Summary and wrap-up

Machine Learning Engineer