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
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