DataMass Gdańsk Summit 2022
phone: +48 570 272 723
e-mail: kamil.piotrowski@evention.pl

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.

 
Requirements
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.
 

Agenda
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
 
Prowadzący:

Machine Learning Engineer
Getindata