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.
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.
Pre-requisites
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
Location
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 support@pipeline.ai for help.
Agenda
Create a Kubernetes cluster with multiple GPUs
Install KubeFlow, Airflow, TFX, and Jupyter
Setup ML Training Pipelines with KubeFlow and Airflow
Transform Data with TFX Transform
Validate Training Data with TFX Data Validation
Train Models with Jupyter, Keras/TensorFlow 2.0, PyTorch, XGBoost, and KubeFlow
Run a Notebook Directly on Kubernetes Cluster with KubeFlow
Analyze Models using TFX Model Analysis and Jupyter
Perform Hyper-Parameter Tuning with KubeFlow
Select the Best Model using KubeFlow Experiment Tracking
Run Multiple Experiments with MLflow Experiment Tracking
Reproduce Model Training with TFX Metadata Store
Deploy the Model to Production with TensorFlow Serving and Istio
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.
#KubeFlow #TensorFlow #MLflow #machine_learning