[Full Day Workshop] KubeFlow + GPU + Keras/TensorFlow 2.0 + TF Extended (TF...

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Hands-on Learning with KubeFlow + GPU + Keras/TensorFlow 2.0 + TF Extended (TFX) + Kubernetes + SageMaker + PyTorch + XGBoost + Airflow + MLflow + Spark + Jupyter

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In this workshop, we build real-world machine learning pipelines using TensorFlow Extended (TFX), KubeFlow, Airflow, and MLflow.

Described in the 2017 paper, TFX is used internally by thousands of Google data scientists and engineers across every major product line within Google.

KubeFlow is a modern, end-to-end pipeline orchestration framework that embraces the latest AI best practices including hyper-parameter tuning, distributed model training, and model tracking.

XGBoost results on the pipelines UI

Airflow is the most-widely used pipeline orchestration framework in machine learning and data engineering.

MLflow is a lightweight experiment-tracking system recently open-sourced by Databricks, the creators of Apache Spark. MLflow supports Python, Java/Scala, and R - and offers native support for TensorFlow, Keras, and Scikit-Learn.


Modern browser - and that's it!

Every attendee will receive a cloud instance

Nothing will be installed on your local laptop

Everything can be downloaded at the end of the workshop


Online Workshop

The link will be sent a few hours before the start of the workshop.

Only registered users will receive the link.

If you do not receive the link a few hours before the start of the workshop, please send your Eventbrite registration confirmation to for help.


1. Create a Kubernetes cluster with multiple GPUs

2. Install KubeFlow, Airflow, TFX, and Jupyter

3. Setup ML Training Pipelines with KubeFlow and Airflow

4. Transform Data with TFX Transform

5. Validate Training Data with TFX Data Validation

6. Train Models with Jupyter, Keras/TensorFlow 2.0, PyTorch, XGBoost, and KubeFlow

7. Run a Notebook Directly on Kubernetes Cluster with KubeFlow

8. Analyze Models using TFX Model Analysis and Jupyter

9. Perform Hyper-Parameter Tuning with KubeFlow

10. Select the Best Model using KubeFlow Experiment Tracking

11. Run Multiple Experiments with MLflow Experiment Tracking

12. Reproduce Model Training with TFX Metadata Store

13. Deploy the Model to Production with TensorFlow Serving and Istio

14. Save and Download your Workspace

Key Takeaways

Attendees will gain experience training, analyzing, and serving real-world Keras/TensorFlow 2.0 models in production using model frameworks and open-source tools.

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