Quantum Machine Learning APIs + Distributed ML with Amazon SageMaker and EFS

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Quantum Machine Learning APIs + Distributed ML with Amazon SageMaker and EFS

August 16, 2021 @ 12:00 pm - 1:00 pm EDT

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

Analytics applications require storage that can scale in capacity and performance to handle workload demands with high throughput coupled with read-after-write consistency and low-latency file operations. Many analytics workloads access large reference data files, libraries and, models through a file interface and require a persistent and shared data store. In this session we show you how to use Amazon EFS as simple, serverless, and set and forget file storage for your machine learning and analytics workloads to achieve low-latency, scale, and still be cost-effective. We will share use cases, common architecture patterns, and best practices.

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