Multiple Dates
[Full Day Workshop] Kubeflow + BERT + GPU + TensorFlow + Keras + SageMaker
Event Information
About this Event
### POSTPONED UNTIL 2021 ###
We are postponing this workshop to 2021 in order to focus on new-content creation.
Here is a link to many of the previous workshops: https://youtube.pipeline.ai
Here is a link to the github repo: https://github.com/data-science-on-aws/workshop
And here is a link to our upcoming book which is now available for pre-order on Amazon.com: https://www.amazon.com/dp/1492079391/
See you in 2021!
### POSTPONED UNTIL 2021 ###
Title
[Full Day Workshop] KubeFlow + BERT + GPU + TensorFlow + Keras + TFX + Kubernetes + PyTorch + XGBoost + Spark + Jupyter + Amazon SageMaker
Description
In this workshop, we build real-world AI and machine learning pipelines using Kubeflow, TensorFlow, TensorFlow Extended (TFX), Keras, PyTorch, and Amazon SageMaker.
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.
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
* Install KubeFlow, TFX, and Jupyter
* Setup Kubeflow Training Pipelines with Keras/TensorFlow 2.0 PyTorch, and XGBoost
* Transform Data with TFX Transform
* Validate Training Data with TFX Data Validation
* 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
* Reproduce Model Training with TFX Metadata Store
* Deploy the Model to Production with TensorFlow Serving and Istio
* Ingest, analyze, and visualize a public dataset with Amazon Athena and S3
* Transform the raw dataset into machine learning features with SageMaker Processing Jobs
* Train a model with our features and SageMaker Training Jobs
* Optimize model training with SageMaker Hyper-parameter Tuning
* Deploy and test our model both online (real-time) and offline (batch) with SageMaker Endpoints
* Automate the entire process with SageMaker and StepFunction Pipelines
* 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.
Related Links
O'Reilly Book: https://datascienceonaws.com
Meetup: https://meetup.com/Advanced-Kubeflow/
GitHub Repo: https://github.com/PipelineAI/
YouTube: https://youtube.pipeline.ai
Slideshare: https://slideshare.pipeline.ai
Support: https://support.pipeline.ai
Monthly Webinar: https://www.eventbrite.com/e/webinar-pipelineai-kubeflow-tensorflow-extended-tfx-airflow-gpu-tpu-spark-ml-tensorflow-ai-tickets-45852865154
