AWS Marketplace Vendor Insights – Simplify Third-Party Software Risk Assessments It helps you to ensure that the third-party software continuously meets your industry standards by compiling security and compliance information, such as data privacy and residency, application security, and access control, in one consolidated dashboard.
As a security engineer, you may now complete third-party software risk assessment in a few days instead of months. You can now:
- Quickly discover products in AWS Marketplace that meet your security and certification standards by searching for and accessing Vendor Insights profiles.
- Access and download current and validated information, with evidence gathered from the vendors’ security tools and audit reports. Reports are available for download on AWS Artifact third-party reports (now available in preview).
- Monitor your software’s security posture post-procurement and receive notifications for security and compliance events.
New for Amazon SageMaker – Perform Shadow Tests to Compare Inference Performance Between ML Model Variants
You can create shadow tests using the new SageMaker Inference Console and APIs. Shadow testing gives you a fully managed experience for setup, monitoring, viewing, and acting on the results of shadow tests. If you have existing workflows built around SageMaker endpoints, you can also deploy a model in shadow mode using the existing SageMaker Inference APIs. You can monitor the progress of the shadow test and performance metrics such as latency and error rate through a live dashboard.
Next Generation SageMaker Notebooks – Now with Built-in Data Preparation, Real-Time Collaboration, and Notebook Automation
The next generation of Amazon SageMaker Notebooks will increase efficiency across the ML development workflow. You can now improve data quality in minutes with the built-in data preparation capability, edit the same notebooks with your teams in real-time, and automatically convert notebook code to production-ready jobs.
SageMaker Studio now offers shared spaces that give data science and ML teams a workspace where they can read, edit, and run notebooks together in real time to streamline collaboration and communication during the development process. Shared spaces provide a shared Amazon EFS directory that you can utilize to share files within a shared space.
You can now select a notebook and automate it as a job that can run in a production environment without the need to manage the underlying infrastructure. When you create a SageMaker Notebook Job, SageMaker Studio takes a snapshot of the entire notebook, packages its dependencies in a container, builds the infrastructure, runs the notebook as an automated job on a schedule you define, and deprovisions the infrastructure upon job completion.
Introducing Support for Real-Time and Batch Inference in Amazon SageMaker Data Wrangler
To build machine learning models, machine learning engineers need to develop a data transformation pipeline to prepare the data. The process of designing this pipeline is time-consuming and requires a cross-team collaboration between machine learning engineers, data engineers, and data scientists to implement the data preparation pipeline into a production environment.
The main objective of Amazon SageMaker Data Wrangler is to make it easy to do data preparation and data processing workloads. With SageMaker Data Wrangler, customers can simplify the process of data preparation and all of the necessary steps of data preparation workflow on a single visual interface. SageMaker Data Wrangler reduces the time to rapidly prototype and deploy data processing workloads to production, so customers can easily integrate with MLOps production environments.
Additional Data Connectors for Amazon AppFlow
AWS announced the addition of 22 new data connectors for Amazon AppFlow, including:
- Marketing connectors (e.g., Facebook Ads, Google Ads, Instagram Ads, LinkedIn Ads).
- Connectors for customer service and engagement (e.g., MailChimp, SendGrid, Zendesk Sell or Chat, and more).
- Business operations (Stripe, QuickBooks Online, and GitHub).
In total, Amazon AppFlow now supports over 50 integrations with various different SaaS applications and AWS services.
Redesigned UI for Amazon SageMaker Studio
The redesigned UI makes it easier for you to discover and get started with the ML tools in SageMaker Studio. One highlight of the new UI includes a redesigned navigation menu with links to SageMaker capabilities that follow the typical ML development workflow from preparing data to building, training, and deploying ML models.
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