Welcome to above the clouds
GCP – Google Cloud Public Sector Summit Day One in Review
Bright and early this morning, we were thrilled to welcome thousands of attendees from government organizations, education institutions, and nonprofits around the globe to our first-ever digital Google Cloud Public Sector Summit. Thomas Kurian, CEO of Google Cloud, and Mike Daniels, Vice President of Global Public Sector at Google Cloud, opened the Summit with a […]
AWS – Detect bias in ML models and explain model behavior with Amazon SageMaker Clarify
Today we are introducing Amazon SageMaker Clarify to help machine learning developers achieve greater visibility into their training data and models so they can identify and limit bias and explain predictions. Read More for the details.
AWS – Introducing Amazon HealthLake to make sense of health data
Amazon HealthLake is a HIPAA-eligible service that enables healthcare providers, health insurance companies, and pharmaceutical companies to store, transform, query, and analyze health data at petabyte scale. Read More for the details.
AWS – Introducing Amazon SageMaker Data Wrangler – The fastest and easiest way to prepare data for machine learning
Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepare data for machine learning (ML) from weeks to minutes. With Amazon SageMaker Data Wrangler, you can simplify the process of data preparation and feature engineering, and complete each step of the data preparation workflow, including data selection, cleansing, exploration, and visualization from […]
AWS – Introducing Amazon SageMaker Feature Store – a fully managed repository to store, discover, share and serve machine learning features
We’re excited to announce Amazon SageMaker Feature Store, a new capability of Amazon SageMaker to ingest, store, share, reuse, and serve features for real time and batch machine learning (ML) applications. Read More for the details.
AWS – Introducing Amazon SageMaker Pipelines, first purpose built CI/CD service for machine learning
We’re excited to announce Amazon SageMaker Pipelines, a new capability of Amazon SageMaker to build, manage, automate, and scale end to end machine learning workflows. SageMaker Pipelines brings automation and orchestration to ML workflows, enabling you to accelerate machine learning projects and scale up to thousands of models in production. Read More for the details.
AWS – AWS Security Hub integrates with AWS Audit Manager for simplified security posture management
AWS Security Hub is now integrated with AWS Audit Manager, which helps simplify how you assess risk and monitor your compliance with regulations and industry standards. AWS Audit Manager is a new service that helps you continuously audit your AWS usage and automates evidence collection to make it easier for you to assess whether your […]
AWS – Amazon AppFlow now provides Amazon Lookout for Metrics connectivity to several cloud applications
Read More for the details.
AWS – Amazon Kendra adds Google Drive connector
Amazon Kendra is a highly accurate and easy to use intelligent search service powered by machine learning. Starting today, AWS customers can automatically index and search content that is contained in Google Drive repositories using Amazon Kendra’s new built-in Google Drive connector. Read More for the details.
AWS – Amazon SageMaker Model Monitor now supports new capabilities to maintain model quality in production
Amazon SageMaker Model Monitor continuously monitors machine learning models for concept drift (i.e. changes in data distribution and characteristics over time) and alerts you if there are any deviations so you can take remedial action. Starting today, you can also use Amazon SageMaker Model Monitor to detect drift in model quality, bias, and feature importance. […]