O'REILLY LIVE ONLINE TRAINING
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 exerts 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.
Kubeflow
- Kubeflow Introduction
- Kubeflow Model Training
- Kubeflow Pipelines
- Kubeflow Model Hyperparameter Tuning & Neural Architecture Search
- Kubeflow Model Serving
MLflow
- MLflow Introduction
- MLflow Model Training
- MLflow Pipelines
- MLflow Model Tuning
- MLflow Model Serving