AWS re:Invent in Review — Part 1

AWS re:Invent is one of the most anticipated events of the year for the global cloud computing community. From major product announcements to technical sessions and partner expos, the event covers both the year in review and what’s yet to come for Amazon Web Services.

With our extensive work on the cloud, there’s no way we’d miss out on what’s happening! We’ll be going over all the major announcements in easy-to-follow weekly roundups, so it’s easier to digest. Keep an eye out for more extensive coverage about some of these announcements early next year!

Week 1 AWS re:Invent Coverage

Amazon SageMaker Integrations

AWS has put in a concerted effort towards integrating Machine Learning (ML) into modern developmental lifecycles with a platform that streamlines the process — Amazon SageMaker. SageMaker Autopilot, a tool that automates the creation of ML models, has now been incorporated into many of its chief data management services:

• ML for Amazon Aurora — Relational Database developers can add ML capabilities to an enterprise application using a simple query. When you run an ML query in Aurora, it can access a range of ML models from Amazon SageMaker and Amazon Comprehend. With a tighter integration between Aurora and every AWS ML service, Amazon pegs the throughput at 100 times faster than moving data between Aurora and SageMaker/Comprehend without this integration.
• ML for Amazon Redshift — Amazon showcased a preview of the capability to run ML algorithms on Amazon Redshift data directly! There’s no need to manually select, build, or train an ML model. When you run an ML query in Amazon Redshift, all selected data is securely exported to Amazon S3 before the training data is cleaned, a model created, and the best model applied by SageMaker Autopilot. All these processes are abstracted away and will happen automatically. Once the model is trained, you can use it as an SQL function.
• ML for Amazon Neptune — Graph Neural Networks are now available for all graph data on Amazon Neptune (using the Deep Graph Library or DGL). This library is specifically built to run deep learning on graph data. It improves the accuracy of most predictions by over 50% compared to traditional ML techniques on the same dataset.

Amazon SageMaker Data Wrangler

Data preparation has long been one of the most time-consuming practices when it comes to the ML process. In fact, Data Scientists say that it takes up a whopping 80% of the total time they spend working on ML problems. SageMaker Data Wrangler is a new update to Amazon SageMaker that allows you to prepare data for ML applications much faster using a visual interface. With just a few clicks, you can connect to data sources (Amazon S3, Athena, Redshift, AWS Lake Formation), explore and visualize data, apply a built-in transformation, export the resulting code to an auto-generated script, and run it on managed infrastructure.

Amazon SageMaker Pipelines

AWS is bringing the power of DevOps to ML projects with the SageMaker Pipelines capability for Amazon SageMaker. This capability will make it easier for data scientists and engineers to build, automate, and scale machine learning pipelines using best-in-class DevOps practices. As we’ve come to expect from SageMaker, all infrastructure is fully managed and doesn’t require any work on your end.

Amazon Elastic Container Service (ECS) Anywhere and Elastic Kubernetes Service (EKS) Anywhere

While AWS Fargate and AWS Outposts did offer customers much-needed flexibility for container deployment, some needed a bit more. Amazon has answered the call. It understands that someone all-in on the cloud might still be bound by financial, legal, regulatory, practical, or even technological constraints that require them to deploy containers outside AWS regions. Amazon ECS’ and EKS’ simplicity will now be extended to deploy native ECS/EKS tasks in any environment, including customer-managed infrastructure, in 2021.

AWS Glue Elastic Views

Glue Elastic Views facilitates building materialized views that combine and replicate data across multiple data stores without any custom code. You can use regular SQL to create a virtual table from numerous different source data stores. Amazon announced support for Amazon DynamoDB, S3, Redshift, and Elasticsearch in the preview that’s now available across some regions. The announcement does promise support for Amazon Relational Database Service (RDS), Aurora, and many more soon.

Amazon CodeGuru Feature Updates

Amazon CodeGuru is a developer tool that helps you improve code quality using ML-powered recommendations and automated code reviews.

Amazon announced three new features for CodeGuru Reviewer and CodeGuru Profiler:

• Python Support for CodeGuru Reviewer and Profiler — Developers working on Python applications can now use CodeGuru to improve their code. This is a welcome update to CodeGuru — expanding its capabilities past Java code and applications.
• Security Detectors for CodeGuru Reviewer — With the addition of a new set of detectors, Amazon CodeGuru Reviewer identifies security vulnerabilities and checks for security best practices in your Java code. The security detectors can find security vulnerabilities in the top 10 OWASP (Open Web Application Security Project) categories, like weak hash encryption.
• Memory Profiling for CodeGuru Profiler — This update is focused on hunting for memory leaks and optimizing how your application uses memory. It offers a new visualization of memory retention — a timeline graph that makes it easier to spot trends and peaks of memory utilization per object type.

AQUA for Amazon Redshift

AQUA, or Advanced Query Accelerator, is a brand-new distributed and hardware-accelerated cache for Amazon Redshift that delivers up to 10x faster query performance. It accelerates Redshifts queries by running data-intensive tasks (like filtering and aggregation) closer to the storage layer. Amazon announced that AQUA-powered Redshift is 100% compatible with Redshift RA3 instances, and you won’t need to change any code to make use of these performance improvements.

Amazon Monitron

Amazon also announced an end-to-end system that uses Machine Learning to detect abnormal behavior in industrial machinery — allowing you to implement predictive maintenance and reducing unplanned downtime. This condition monitoring service will track your equipment’s health using sensors to ensure you can easily capture relevant data.

Amazon QuickSight Q

Amazon QuickSight, a powerful Business Intelligence service for the cloud, also got an exciting update during AWS re:Invent — QuickSight Q. It empowers business users to ask questions about their data using everyday natural language to get answers in seconds. No coding, no formats, or syntaxes to follow. Q uses Deep Learning and Machine Learning to understand user intent and derives meaning from underlying data to generate comprehensive answers using relevant visualizations.

Updates are already flying in from Week 2 of AWS re:Invent. We’ll get some coverage on those announcements to you soon!