Machine Learning Operations (MLOps)
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.
Requirements
Some experience coding in Python, basic understanding of cloud computing and machine learning concepts.
Training outcome
During the course we simulate real-world end-to-end scenarios – building a Machine Learning pipeline to train a model, and deploy it on 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.
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.
Course agenda*
Part 1
Introduction to Machine Learning Operations (MLOps)
Introduction and key concepts
MLOps components
The challenges of deploying and maintaining Machine Learning models on production
The Machine Learning model lifecycle
Part 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
Part 3
Kubeflow and Kubeflow Pipelines
Introduction and key concepts
Example of Kubeflow Pipelines (managed) and Vertex AI (serverless) deployments
Hands-on exercises
Part 4
Building infrastructure for your Machine Learning platform
Overview of MLOps Frameworks landscape, and reference architectures
Part 5
Summary and wrap-up
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Testimonials
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