Data Science on AWS (San Francisco, Global)

RSVP Webinar: https://www.eventbrite.com/e/1-hr-free-workshop-pipelineai-gpu-tpu-spark-ml-tensorflow-ai-kubernetes-kafka-scikit-tickets-45852865154

Zoom link: https://us02web.zoom.us/j/82308186562

Talk #1: Introductions and Meetup Announcements (Chris Fregly and Antje Barth)

Talk #2: Quantum Machine Learning APIs (Raouf Dridi, Senior Developer at Quantum Computing Inc)
* How did you get into quantum computing?
* What is most fun about your job?
* Tell us about the Quantum ML APIs that are available today such as QCI's API, PennyLane, Qiskit, TensorFlow Quantum
* Show us a demo on how QCI integrates with Amazon Braket and other quantum services (or whatever you want to highlight in a demo)
* Where are Quantum ML APIs heading?
* How can Quantum-newbies learn more about Quantum machine learning?

More quantum resources:
* Minimizing polynomial functions on quantum computers (Dwave):
https://arxiv.org/abs/1903.08270

* Prime factorization (Dwave)
https://www.nature.com/articles/srep43048

* Compiling gate model circuits via re rewriting systems/Knuth-Bendix
https://arxiv.org/abs/1905.00129

* Compiling in quantum annealing (Dwave)
https://arxiv.org/abs/1912.08314

Talk #3: Distributed ML Use Cases using Amazon Elastic File System (EFS) by Ananth Vaidyanathan, Senior Product Manager Amazon EFS

RSVP Webinar: https://www.eventbrite.com/e/1-hr-free-workshop-pipelineai-gpu-tpu-spark-ml-tensorflow-ai-kubernetes-kafka-scikit-tickets-45852865154

Zoom link: https://us02web.zoom.us/j/82308186562

Meetup: https://meetup.datascienceonaws.com

Related Links
=============
O'Reilly Book: https://www.amazon.com/dp/1492079391/
Website: https://datascienceonaws.com
Meetup: https://meetup.datascienceonaws.com
GitHub Repo: https://github.com/data-science-on-aws/
YouTube: https://youtube.datascienceonaws.com
Slideshare: https://slideshare.datascienceonaws.com
Support: https://support.pipeline.ai

RSVP Webinar: https://www.eventbrite.com/e/1-hr-free-workshop-pipelineai-gpu-tpu-spark-ml-tensorflow-ai-kubernetes-kafka-scikit-tickets-45852865154

Zoom link: https://us02web.zoom.us/j/82308186562

Talk #1: Hierarchical Forecasting with scikit-hts and Amazon SageMaker by Mani Khanuja and Farooq Sabir, Senior Solution Architects for AI/ML @ AWS

Time series forecasting is a very common and well known problem in machine learning and statistics. Most of the times, the time series data follows a hierarchical aggregation structure. For e.g. in retail, weekly sales for a SKU at a store can roll up to different geographical hierarchies at city, state or country level. In these cases we need to ensure, that the sales estimates are in agreement, when rolled up to a higher level.

In such scenarios, Hierarchical Time Series Forecasting, which is the process of generating coherent forecasts (or reconciling incoherent forecasts), allowing individual time series to be forecast individually, but preserving the relationships within the hierarchy, is used.
Many customers are either using hierarchical forecasting methods or have an upcoming use case that requires hierarchical forecasting to achieve better results. In this session, we will demonstrate, how to set up data for hierarchical forecasting, and use Prophet model to carry out forecasting using SageMaker framework and features.

Bios:
Mani Khanuja is an Artificial Intelligence and Machine Learning Specialist SA at Amazon Web Services (AWS). She helps customers using machine learning to solve their business challenges using the AWS. She spends most of her time diving deep and teaching customers on AI/ML projects related to computer vision, natural language processing, forecasting, ML at the edge, and more.

Farooq Sabir is an Artificial Intelligence and Machine Learning Specialist Solutions Architect at Amazon Web Services (AWS). He works with customers to solve problems in artificial intelligence, computer vision, machine learning and optimization domains. Prior to joining AWS, he has worked as a lead data scientist at AT&T. He holds a PhD in Electrical Engineering from The University of Texas at Austin

Talk #2: Building a recommendation engine for online courses on AWS by Kesha Williams, Principal Training Architect @ ACloudGuru

Talk #3: TBD

RSVP Webinar: https://www.eventbrite.com/e/1-hr-free-workshop-pipelineai-gpu-tpu-spark-ml-tensorflow-ai-kubernetes-kafka-scikit-tickets-45852865154

Zoom link: https://us02web.zoom.us/j/82308186562

Meetup: https://meetup.datascienceonaws.com

Related Links
=============
O'Reilly Book: https://www.amazon.com/dp/1492079391/
Website: https://datascienceonaws.com
Meetup: https://meetup.datascienceonaws.com
GitHub Repo: https://github.com/data-science-on-aws/
YouTube: https://youtube.datascienceonaws.com
Slideshare: https://slideshare.datascienceonaws.com
Support: https://support.pipeline.ai

Workshop: Build an AI/ML pipeline with BERT, TensorFlow and Amazon SageMaker

RSVP: https://www.eventbrite.com/e/full-day-workshop-kubeflow-bert-gpu-tensorflow-keras-sagemaker-tickets-63362929227

**Description**

In this hands-on workshop, we will build an end-to-end AI/ML pipeline for natural language processing with Amazon SageMaker.

You will learn how to:

• Ingest data into S3 using Amazon Athena and the Parquet data format
• Visualize data with pandas, matplotlib in Jupyter notebooks
• Run data bias analysis with SageMaker Clarify
• Perform feature engineering on a raw dataset using Scikit-Learn and SageMaker Processing Jobs
• Store and share features using SageMaker Feature Store
• Train and evaluate a custom BERT model using TensorFlow, Keras, and SageMaker Training Jobs
• Evaluate the model using SageMaker Processing Jobs
• Track model artifacts using Amazon SageMaker ML Lineage Tracking
• Run model bias and explainability analysis with SageMaker Clarify
• Register and version models using SageMaker Model Registry
• Deploy a model to a REST Inference Endpoint using SageMaker Endpoints
• Automate ML workflow steps by building end-to-end model pipelines using SageMaker Pipelines

**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

Related Links
=============
O'Reilly Book: https://www.amazon.com/dp/1492079391/
Website: https://datascienceonaws.com
Meetup: https://meetup.datascienceonaws.com
GitHub Repo: https://github.com/data-science-on-aws/
YouTube: https://youtube.datascienceonaws.com
Slideshare: https://slideshare.datascienceonaws.com
Support: https://support.pipeline.ai

RSVP Webinar: https://www.eventbrite.com/e/1-hr-free-workshop-pipelineai-gpu-tpu-spark-ml-tensorflow-ai-kubernetes-kafka-scikit-tickets-45852865154

Zoom link: https://us02web.zoom.us/j/82308186562

Talk #1: TBD

Talk #2: TBD

Talk #3: TBD

RSVP Webinar: https://www.eventbrite.com/e/1-hr-free-workshop-pipelineai-gpu-tpu-spark-ml-tensorflow-ai-kubernetes-kafka-scikit-tickets-45852865154

Zoom link: https://us02web.zoom.us/j/82308186562

Meetup: https://meetup.datascienceonaws.com

Related Links
=============
O'Reilly Book: https://www.amazon.com/dp/1492079391/
Website: https://datascienceonaws.com
Meetup: https://meetup.datascienceonaws.com
GitHub Repo: https://github.com/data-science-on-aws/
YouTube: https://youtube.datascienceonaws.com
Slideshare: https://slideshare.datascienceonaws.com
Support: https://support.pipeline.ai