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Building an ML/AI Pipeline with Kubeflow and MLflow

Continuous machine learning and AI

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Topic: Data
Chris Fregly
Antje Barth

Kubeflow and MLflow are open source projects dedicated to end-to-end machine learning using the latest AI best practices, including hyperparameter tuning, AutoML, and experiment tracking, to find the best algorithms and models to fit your dataset.

Join experts Chris Fregly and Antje Barth to learn how to build a real-world machine learning pipeline using Kubeflow, MLflow, TensorFlow, Keras, and Apache Spark in a Kubernetes environment. Along the way, you’ll explore model deploying, A/B testing, and multiarmed bandits—tools to help you quickly deploy your models into production with zero downtime.

What you'll learn-and how you can apply it

By the end of this live online course, you’ll understand: - The core components of the Kubeflow ecosystem - Modern best practices for ML/AI model training, tuning, and deploying - How to build your own Kubeflow pipeline for your machine learning use cases - How to deploy a Jupyter notebook to a Kubernetes cluster - How to use hyperparameter tuning and AutoML to find the best models - How to use experiment tracking to visually compare model performance - How to safely deploy models directly to production with zero downtime - How to compare models in production using A/B testing and multiarmed bandits

And you’ll be able to: - Set up continuous machine learning pipelines with Kubeflow and MLflow - Train models using Jupyter, TensorFlow, Keras, PyTorch, and Apache Spark - Perform hyperparameter tuning to find the best model - Compare models using experiment tracking - Deploy models directly to production with Kubeflow Serving and Istio - Use A/B testing and multiarmed bandits with production models

This training course is for you because...

  • You’re a data scientist or ML engineer who needs to develop AI/ML pipelines for production applications.
  • You’re a data engineer who needs to manage AI/ML pipelines in production.
  • You’re a data analyst or business intelligence specialist who wants to understand the landscape of end-to-end AI/ML pipelines.

Prerequisites

  • A computer with a modern web browser installed (Chrome is recommended, but Firefox is OK.)

About your instructors

  • Chris Fregly is a San Francisco, California-based developer advocate for AI and machine learning at Amazon Web Services (AWS). He’s worked with Kubeflow and MLflow since 2017 and founded the global Advanced Kubeflow Meetup. Chris regularly speaks at ML/AI conferences across the world, including the O’Reilly AI and Strata Data Conferences. Previously, Chris was founder at PipelineAI, helping startups and enterprises continuously deploy AI and machine learning pipelines using Kubeflow and MLflow, and was an ML-focused engineer at both Netflix and Databricks.

  • Antje Barth is a Düsseldorf, Germany-based developer advocate for AI and machine learning at Amazon Web Services (AWS). She cofounded a chapter of Women in Big Data in Germany and regularly speaks at ML/AI conferences across the world, including the O’Reilly AI Conference. She’s been working with Kubeflow since 2018 and is passionate about helping developers leverage big data, Docker containers, and Kubernetes platforms in the context of AI and machine learning. Previously, Antje was a big data and ML engineer at both MapR and Cisco.

Schedule

The timeframes are only estimates and may vary according to how the class is progressing

Introduction (20 minutes) - Presentation: Exploring the environment - Hands-on exercise: Set up and explore the environment - Q&A

Kubeflow overview (20 minutes) - Presentation: Kubeflow Pipelines use cases - Hands-on exercise: Create a Kubeflow pipeline with TensorFlow - Q&A

MLflow overview (15 minutes) - Presentation: MLflow pipelines use cases - Hands-on exercise: Create an MLflow pipeline with Apache Spark and Keras - Q&A

Break (5 minutes)

Hyperparameter tuning (15 minutes) - Presentation: Hyperparameter tuning use cases - Hands-on exercise: Perform hyperparameter tuning - Q&A

AutoML (15 minutes) - Presentation: AutoML use cases - Hands-on exercise: Implement AutoML - Q&A

Experiment tracking (15 minutes) - Presentation: Experiment tracking with Kubeflow and MLflow use cases - Hands-on exercise: Track and compare experiments - Q&A

Break (15 minutes)

Model deploying (15 minutes) - Presentation: Model deploying options - Hands-on exercise: Deploy two versions of a model using Kubeflow Serving and Istio

A/B testing (15 minutes) - Presentation: A/B testing use cases - Hands-on exercise: Perform an A/B test with the two versions of the model

Multiarmed bandit testing (15 minutes) - Presentation: Multiarmed bandit testing use cases - Hands-on exercise: Perform a multiarmed bandit test with the two versions of the model

Wrap-up and Q&A (15 minutes)