Today we’re kicking off our annual re:invent series joined by Swami Sivasubramanian, VP of Artificial Intelligence, at AWS.
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During re:Invent last week, Amazon made a ton of announcements on the machine learning front, including quite a few advancements to SageMaker. In this roundup conversation, we discuss the motivation for hosting the first-ever machine learning keynote at the conference, a bunch of details surrounding tools like Pipelines for workflow management, Clarify for bias detection, and JumpStart for easy to use algorithms and notebooks, and many more.
We also discuss the emphasis placed on DevOps and MLOps tools in these announcements, and how the tools are all interconnected. Finally, we briefly touch on the announcement of the AWS feature store, but be sure to check back later this week for a more in-depth discussion on that particular release!
Thanks to our Sponsor!
Before we get into today’s episode, I want to send a huge thanks to our friends at Amazon Web Services for their support of the podcast and sponsorship of this year’s re:Invent series.
AWS offers a vast array of machine learning services and supporting cloud infrastructure, putting machine learning in the hands of every developer, data scientist, and expert practitioner. It’s extensive set of machine learning services at all three layers of the technology stack offers a broad array capabilities including contact center intelligence, dev/ops tools, industrial machine learning, enterprise search, health analytics, and much more. And, Amazon SageMaker – one of the fastest growing services in AWS history – helps data scientists and developers to prepare, build, train, and deploy high-quality machine learning models quickly by bringing together a broad set of capabilities purpose-built for ML. To learn more about AWS machine learning services, and how they’re helping tens of thousands of customers accelerate their machine learning journeys, visit https://aws.amazon.com/machine-learning/.
- Blog: Amazon SageMaker Edge Manager Simplifies Operating Machine Learning Models on Edge Devices
- Blog: Amazon SageMaker Clarify Detects Bias and Increases the Transparency of Machine Learning Models
- Blog: Profile Your Machine Learning Training Jobs With Amazon SageMaker Debugger
- Blog: Managed Data Parallelism in Amazon SageMaker Simplifies Training on Large Datasets
- Blog: Amazon SageMaker Simplifies Training Deep Learning Models With Billions of Parameters
- AWS News Blog
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- Check out the official TWIMLcon:AI Platform video packages here!
- Download our latest eBook, The Definitive Guide to AI Platforms!
“More On That Later” by Lee Rosevere licensed under CC By 4.0