Important findings from Swami Sivasubramanian’s speech at AWS re: Invent 2021

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Swami Sivasubramanian is the vice president of machine learning at AWS. He joined Amazon in 2005 as an engineer and has since built over 40 AWS services with his team. His expertise in cloud computing and machine learning has helped him rise the ranks over the years. At the ongoing AWS re: Invent 2021 event, Sivasubramanian showcased some of AWS ‘most fascinating machine learning product launches. We list some of the most important ones.

Amazon DevOps Guru for RDS

It’s a new machine learning-based feature for Amazon’s Relational Database Service (RDS). It can automatically detect and diagnose database performance and operational problems, helping users resolve bottlenecks in much less time. This new feature builds on the existing capabilities of DevOps Guru to provide recommendations for addressing a variety of database-related issues. The DevOps Guru for RDS notifies developers and DevOps engineers, provides information and details about the problem, and suggests intelligent recommendations for resolution.

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AWS Database Migration Service (DMS) fleet advisor

Sivasubramanian has introduced AWS Database Migration Service (DMS) Fleet Advisor to simplify and accelerate the transfer of data to the cloud and the comparison with the corresponding database service. It automatically creates an inventory of local databases and analytics services by streaming it to Amazon S3. The AWS team then analyzes the data to match it against the appropriate amount of AWS Datastore. This helps in providing customized migration plans.

This approach is more cost effective because users no longer have to rely on outside consultants to move the data. It also makes it much easier to modernize your data infrastructure with powerful relational databases.

New SageMaker features

AWS CEO Adam Selipsky presented in his keynote SageMaker Canvas, a no-code platform to create models for machine learning and generate accurate predictions. AWS wasn’t done with SageMaker; In his keynote presentation, Sivasubramanian SageMaker announced a few more features:

SageMaker Ground Truth Plus: It is a service that uses teams of experts to provide high quality training data sets more quickly. It uses a labeling workflow, including ML techniques for active learning, machine validation, and pre-training.

SageMaker inference recommender: It is a tool to help users choose the best available compute instance to deploy ML models for better performance and lower cost. It automatically selects the appropriate type of computing instance, the number, the model optimizations and the container parameters.

SageMaker Serverless Interface: It enables ML models to be easily deployed for inference without the need to configure and manage the underlying infrastructure.

SageMaker Training Compiler: It uses GPU instances more efficiently, which speeds up the training of deep learning models by up to 50 percent. These are deep learning models from high-level language representations to hardware-optimized instructions.

SageMaker Studio Lab: It is a free service for developers to learn machine learning tools and techniques. With this new platform, customers can focus on the data science aspect of machine learning without having to set up or configure any infrastructure. It is based on the JupyterLab web application and users are allowed to use any framework such as PyTorch, MxNet, TensorFlow, NumPy etc. Another benefit of using SageMaker Studio Lab is its integration with Github, which allows customers to open, view, and edit any notebook.

See also

Amazon Kendra Experience Builder

Amazon Kendra is a machine learning based intelligent search service. Sivasubramanian announced three new features in his keynote speech:

Amazon Kendra Experience Builder: It is a low- / no-code platform for customers who can provide a fully functional and customizable search experience without prior experience with machine learning. It provides an intuitive visual workflow for creating and launching a Kendra-based search application in the cloud. It comes with AWS Single Sign-On (SSO) integration that supports popular identity providers like Azure AD and Okta while providing secure SSO for end users.

Amazon Kendra Search Analytics Dashboard: It helps administrators and content creators understand how end users find relevant search results, their quality and the gaps in the content. It should help to better understand the quality and usability metrics in your own Kendra-based search applications. This dashboard provides a snapshot of user interaction with search applications that could be viewed in a visual dashboard in the console.

Enrichment of custom Amazon Kendra documents: Users can create a custom ingestion pipeline to preprocess documents before they are indexed in Kendra. Enrichment is done through simple rules that can be configured in the console or by calling functions from Amazon Lambda.


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Shraddha Goled

Shraddha Goled

I am a journalist with a postgraduate degree in Computer Network Engineering. When I’m not reading or writing, I can be found scribbling to my heart’s content.


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