Amazon Relational Database Service (Amazon RDS) Custom is a managed database service for legacy, custom, and packaged applications that require access to the underlying OS and DB environment. Amazon RDS Custom is now available for the SQL Server database engine. Amazon RDS Custom for SQL Server automates setup, operation, and scaling of databases in the cloud while granting access to the database and underlying operating system to configure settings, install drivers, and enable native features to meet the dependent application’s requirements.

AWS Database Migration Service (AWS DMS) is a service that helps you migrate databases to AWS quickly and securely. AWS DMS Fleet Advisor is a new feature of AWS DMS that allows you to quickly build a database and analytics migration plan by automating the discovery and analysis of your fleet. AWS DMS Fleet Advisor is intended for users looking to migrate a large number of database and analytic servers to AWS.

Today, we are excited to announce Amazon SageMaker Training Compiler, a new feature of SageMaker that can accelerate the training of deep learning (DL) models by up to 50% through more efficient use of GPU instances.

Amazon SageMaker Serverless Inference is a new inference option that enables you to easily deploy machine learning models for inference without having to configure or manage the underlying infrastructure. Simply select the serverless option when deploying your machine learning model, and Amazon SageMaker automatically provisions, scales, and turns off compute capacity based on the volume of inference requests. With SageMaker Serverless Inference, you pay only for the duration of running the inference code and the amount of data processed, not for idle time.

Amazon SageMaker Inference Recommender helps you choose the best available compute instance and configuration to deploy machine learning models for optimal inference performance and cost.

Amazon SageMaker Studio is the first fully integrated development environment (IDE) for machine learning (ML). It provides a single, web-based visual interface where you can perform all ML development steps required to prepare data, as well as to build, train, and deploy models. We recently introduced   the ability to visually browse and connect to Amazon EMR clusters right from the SageMaker Studio notebook. Starting today, you can now monitor and debug your Apache Spark jobs running on EMR right from SageMaker Studio notebooks with just a click. Additionally, you can now discover, connect to, create, terminate and manage EMR clusters directly from SageMaker Studio. The built-in integration with EMR therefore enables you to do interactive data preparation and machine learning at peta-byte scale right within the single universal SageMaker Studio notebook.

Amazon Kendra is an intelligent search service powered by machine learning. Today, we are excited to announce the launch of three new features: (1) Experience Builder to create fully functional search applications in a few clicks, (2) Search Analytics Dashboard for search insights and metrics, and (3) Custom Document Enrichment for document pre-processing and enrichment during ingestion.

Introducing Amazon SageMaker Studio Lab is a free, no-configuration service that allows developers, academics, and data scientists to learn and experiment with machine learning.

We are excited to announce the preview of automatic chatbot designer in Amazon Lex, enabling developers to automatically design chatbots from conversation transcripts in hours rather than weeks. Amazon Lex helps you build, test, and deploy chatbots and virtual assistants on contact center services (such as Amazon Connect), websites, and messaging channels (such as Facebook Messenger). The automatic chatbot designer enhances the usability of Amazon Lex by automating conversational design, minimizing developer effort and reducing the time it takes to design a chatbot.

The AWS Artificial Intelligence (AI) and Machine Learning (ML) Scholarship program, in collaboration with Intel and Udacity, provides students who self-identify as underserved and underrepresented in tech educational content, career mentorship programs, and 2,500 scholarships annually as part of a commitment to a more diverse future AI & ML workforce.