AWS re:Invent kicked off a slate of announcements from the hyperscaler and global juggernaut, and unsurprisingly, GenAI and new solutions geared towards data management are the key focus.
Amazon SageMaker Lakehouse unveiled to simplify analytics
Amazon SageMaker is the company’s unified platform for data, analytics and AI. It offers a unified view of widely adopted AWS machine learning and analytics capabilities and provides an integrated approach across AI adoption paths.
Now, with the creation of Lakehouse, the platform unifies data across Amazon Simple Storage Service (Amazon S3) data lakes and Amazon Redshift data warehouses. This will enable partners and customers to “build powerful analytics and artificial intelligence and machine learning (AI/ML) applications on a single copy of data,” according to the AWS blog post.
Amazon SageMaker gains new capabilities and deeper integrations
In addition to announcing Lakehouse, the SageMaker platform has also been enhanced into what the company calls the “next generation” of its platform technology. What was previously the entirety of SageMaker is now “Amazon SageMaker AI,” just one part of a broader effort to address a variety of data, AI/ML, and other needs. The newly minted Amazon SageMaker AI will remain available as a standalone tool for those interested in only its capabilities.
AWS touts the following as highlights of the new Amazon SageMaker:
- Amazon SageMaker Unified Studio (available now to preview) – Build with all your data and tools for analytics and AI in a single environment.
- Amazon SageMaker Lakehouse – Unify data across Amazon Simple Storage Service (Amazon S3) data lakes, Amazon Redshift data warehouses, and third-party and federated data sources with Amazon SageMaker Lakehouse.
- Data and AI Governance – Securely discover, govern, and collaborate on data and AI with Amazon SageMaker Catalog, built on Amazon DataZone.
- Data Processing – Analyze, prepare, and integrate data for analytics and AI using open source frameworks on Amazon Athena, Amazon EMR, and AWS Glue.
- Model development – Build, train, and deploy ML and foundation models (FMs) with fully managed infrastructure, tools, and workflows with Amazon SageMaker AI.
- Generative AI app development – Build and scale generative AI applications with Amazon Bedrock.
- SQL analytics – Gain insights through Amazon Redshift.
Amazon Q Business adds workflow automation
Amazon Q Busines, GenAI Assistant released earlier this year to drive productivity across business applications, now supports over 50 integrations. These include Microsoft Teams, PagerDuty Advance, Salesforce, ServiceNow, and more.
The other announcement related to Amazon Q Business is a forthcoming capability to provide detailed workflow plans.
According to the announcement, users will “only need to describe your desired workflow using natural language, upload a standard operating procedure (SOP), or record a video of the process being performed. Amazon Q Business uses generative AI to automatically author a detailed workflow plan from your inputs in minutes. Then, with the recommended workflow, you can review, test, modify, or approve.”
Is AWS catching up in the AI race?
The announcements follow months of speculation that AWS was falling behind competitors in the race to build and sell GenAI solutions. In an interview with TechCrunch ahead of re:Invent, AWS CEO Matt Garman said the conference would not exclusively focus on AI as the company focuses on innovation “across the entire stack.”
From a new CEO to additional partnerships, AWS grew significantly in 2024. Read more about the hyperscaler’s moves in Channel Insider’s recap.