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.
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.
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.
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)
Session #2 - Kedro - a framework to structure your ML pipeline
Session #3 - Kubeflow and Kubeflow Pipelines
Session #4 - Building infrastructure for your Machine Learning platform
Session #5 - Summary and wrap-up