New SageMaker Capabilities
AWS announced a host of new capabilities for Amazon SageMaker, its fully managed ML service that allows data scientists and developers to quickly build and train ML models and deploy them into a production-ready hosted environment.
“Data and AI/ML product line expansions in the SageMaker product line are quite relevant, as the usage of ML grows exponentially,” said Tata Consultancy’s Mohan. “This is an area that can drive the most consumption, because customers will see the immediate and tangible value of cloud platform adoption.”
Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepare data for ML from weeks to minutes, according to AWS. Amazon SageMaker Feature Store is a fully managed, purpose-built data store for storing, updating, retrieving and sharing ML features. Amazon SageMaker Pipelines is the first purpose-built, easy-to-use continuous integration and continuous delivery (CI/CD) service for ML, according to AWS.
Amazon SageMaker Clarify provides developers with greater visibility into their training data, so they can detect bias in ML models and understand model predictions. Deep profiling for Amazon SageMaker Debugger enables developers to train their models faster by automatically monitoring system resource utilization and providing alerts for training bottlenecks. Distributed Training on Amazon SageMaker can train large, complex deep learning models up to two times faster than current ML processors, according to AWS.
Amazon SageMaker Edge Manager allows developers to optimize, secure, monitor and maintain ML models deployed on fleets of edge devices such as mobile devices, smart cameras, robots and personal computers. Amazon SageMaker JumpStart helps users bring ML applications to market with a developer portal that includes pre-trained models and pre-built workflows.
Customers are struggling with how to approach AI and ML because the technology is not the value – the outcome or insight is, and use cases drive the adoption, said John Tweardy, chief technology officer and strategic growth leader for the strategy and analytics practice at Deloitte Consulting.
“Technology is a partner on this journey, but the business must own the patterns, models and adoption rate and then, together, change the culture,” Tweardy said. “The multiple releases to SageMaker with Data Wrangler, Pipelines and Feature Store all make the building and maintenance of the models easier and more reusable. This should support faster adoption and speed, and enable data scientists to take on more complex challenges.”
AWS‘ breadth and pace of innovation across the stack continues to impress, and that’s especially evident in what AWS rolled out in its analytics and ML portfolio, said Prat Moghe, CEO of Cazena, a Waltham, Mass.-based AWS Advanced Technology Partner that offers instant cloud data lakes.
“Innovations like Amazon SageMaker Data Wrangler for preparing data for machine learning, AWS Glue Elastic Views, SageMaker Pipelines and beyond are the reason AWS now has more than 100,000 customers using its cloud for machine learning, the reason it has the largest number of data lake instances, etc.,” Moghe said.