Hands-on Learning withKubeFlow + Keras/TensorFlow 2.0 + TF Extended (TFX) + Kubernetes + PyTorch + XGBoost + Airflow + MLflow + Spark + Jupyter + TPU
In this workshop, we build real-world machine learning pipelines using TensorFlow Extended (TFX), KubeFlow, & Airflow.
Described in the 2017 paper, TFX is used internally by thousands of Google data scientists & 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, & model tracking.
Airflow is the most-widely used pipeline orchestration framework in machine learning.
Modern browser - & 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
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 firstname.lastname@example.org help.
1. Create a Kubernetes cluster
2. Install KubeFlow, Airflow, TFX, & Jupyter
3. Setup ML Training Pipelines with KubeFlow & 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, & KubeFlow
7.Run a Notebook Directly on Kubernetes Cluster with KubeFlow
8. Analyze Models using TFX Model Analysis & Jupyter
9. Perform Hyper-Parameter Tuning with KubeFlow
10. Select the Best Model using KubeFlow Experiment Tracking
11. Reproduce Model Training with TFX Metadata Store & Pachyderm
12.Deploy the Model to Production with TensorFlow Serving & Istio
13. Save & Download your Workspace
Attendees will gain experience training, analyzing, & serving real-world Keras/TensorFlow 2.0 models in production using model frameworks & open-source tools.
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