Highlights From AWS re:Invent 2021 Database, Analytics, and Machine Learning Keynote With Swami Sivasubramanian

Vice President of Machine Learning at AWS, Dr. Swami Sivasubramanian’s keynote speech on Wednesday, Dec. 1, kicked off some big announcements at re:Invent 2021 and reaffirmed AWS’s industry role as the undisputed champion in machine learning and data analytics.

If you’ve been paying attention, you know Machine Learning (ML) isn’t just a buzzword to sell more cloud. As Dr. Sivasubramanian noted, “80% of data today is unstructured,” and if you want to process unstructured data, you must create systems capable of handling it. Machine Learning is the solution for the high volume of data created and processed every minute of every day.

The keynote focused on three keystones of data analytics and machine learning: storage and access, analysis and visualization, and predictive analysis. To that end, Dr. Sivasubramanian introduced several enhancements to Amazon’s already impressive ML capabilities.

Amazon DevOps Guru for RDS

The benefits of a managed database like Amazon Aurora have been evident for some time. Better performance, less overhead, easier administration and provisioning, and less expensive to run in many cases. But up until now, diagnosing and fixing database issues has still been a challenge for many organizations.

Using machine learning models informed by years of data collection and analytics, Amazon DevOps Guru for RDS can automatically detect, diagnose, and resolve many database-related performance issues in minutes. The service automatically identifies, performs root cause analysis, and recommends corrective action. In some cases, it will automatically remediate issues.

Amazon DynamoDB Standard-Infrequent Access Table Class

The new, cost-effective table class stores infrequently accessed data while maintaining high availability performance. The DynamoDB Standard-IA table class reduces storage costs by up to 60%, and because it’s DynamoDB, you can switch between table and classes at any time.

AWS Database Migration Service (DMS) Fleet Advisor

AWS provides a vast portfolio of database services, from standards such as Amazon RDS and ElastiCache to more exotic and purpose-built services such as Amazon Athena, Amazon Neptune, and many others. AWS Database Migration Service Fleet Advisor allows you to migrate fleets of databases easily and quickly, with automated inventory and migration recommendations. AWS Database Migration Service Fleet Advisor makes it possible to migrate and modernize with the best purpose-built database.

Amazon SageMaker Updates

Introduced in 2017, Amazon SageMaker is a machine learning platform that enables developers to build, train, and deploy cloud learning models with a minimum amount of coding. These updates keep SageMaker the go-to machine learning platform.

Amazon SageMaker Ground Truth Plus builds on the success of SageMaker Ground Truth by delivering high-quality training datasets quickly and reducing the cost of expert data labeling. An assembled team of AWS experts builds your project.

Amazon SageMaker Studio Notebook provides data engineering, analytics, and machine learning workflows in a single notebook. Using SageMaker Studio Notebook, you can spin up a notebook within minutes, connect to a data source, write and perform queries, and then create and deploy your model.

Amazon SageMaker Training Compiler is the first of three SageMaker infrastructure updates. It lets you use the underlying instance GPU to run your code more efficiently, accelerating model training speed by up to 50%.

Amazon SageMaker Inference Recommender, the second infrastructure update, reduces the time to deploy from weeks to hours by helping you choose the optimal configurations for deploying your machine learning models.

Amazon SageMaker Serverless Inference, the final infrastructure update, lowers the cost of ownership with pay-per-use pricing and eliminates the need to launch the underlying infrastructure. Instead, you pay only for what you use, and your infrastructure is automatically unallocated when you are finished.

Amazon Kendra Experience Builder

Amazon Kendra is a search service powered by machine learning, designed to help organizations easily find structured or unstructured data scattered across multiple locations and multiple data sources. Amazon Kendra Experience Builder enhances this service by allowing you to deploy Amazon Kendra search applications with just a few clicks. No coding is required. The application is created through a drag and drop visual workflow.

Amazon Lex Automated Chatbot Designer

Amazon Lex Automated Chatbot Designer works with Amazon Lex to use machine learning to provide a base chatbot with advanced natural language understanding. It does this by analyzing chat transcripts with agents and callers and building out a conversational model.

Amazon SageMaker Studio Lab

Of all journeys into the cloud, machine learning and data analytics are arguably more challenging paths. Amazon SageMaker Studio Lab is a learning environment to learn machine learning technology at no cost, and projects can eventually be imported into Amazon SageMaker.

Dr. Sivasubramanian’s keynote was a passionate commitment to the future of machine language and data analytics in the AWS cloud. This keynote highlighted Amazon’s commitment to understanding how customers and partners use, visualize, and process data and then building the foundational services and features to keep the AWS ecosystem relevant and forward-looking.

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