Amazon Web Services776 тыс
Опубликовано 26 июля 2018, 20:19
Learn more about the AWS Twitch Channel at - amzn.to/2LqP3rJ.
The AWS Summit was a free event designed to bring together the cloud computing community to connect, collaborate, and learn about AWS. AWS live streamed the event with the AWS Launchpad, featuring technical discussions, announcements, demos, customer interviews and live Q&A with the Twitch community.
SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker removes all the barriers that typically slow down developers who want to use machine learning. Machine learning often feels a lot harder than it should be to most developers because the process to build and train models, and then deploy them into production is too complicated and too slow. Amazon SageMaker removes the complexity that holds back developer success with each of these steps and includes modules that can be used together or independently to build, train, and deploy your machine learning models.
Learn more here: aws.amazon.com/sagemaker
Host:
- Randall Hunt, Sr. Technical Evangelist, AWS
Guests:
- Sunil Mallya, Senior Solutions Architect, ML Solutions Lab, AWS
- Shyam Srinivasan, Sr. Product Marketing Manager, AWS AI Services
The AWS Summit was a free event designed to bring together the cloud computing community to connect, collaborate, and learn about AWS. AWS live streamed the event with the AWS Launchpad, featuring technical discussions, announcements, demos, customer interviews and live Q&A with the Twitch community.
SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker removes all the barriers that typically slow down developers who want to use machine learning. Machine learning often feels a lot harder than it should be to most developers because the process to build and train models, and then deploy them into production is too complicated and too slow. Amazon SageMaker removes the complexity that holds back developer success with each of these steps and includes modules that can be used together or independently to build, train, and deploy your machine learning models.
Learn more here: aws.amazon.com/sagemaker
Host:
- Randall Hunt, Sr. Technical Evangelist, AWS
Guests:
- Sunil Mallya, Senior Solutions Architect, ML Solutions Lab, AWS
- Shyam Srinivasan, Sr. Product Marketing Manager, AWS AI Services
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