AWS News Blog
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New – Amazon SageMaker Pipelines Brings DevOps Capabilities to your Machine Learning Projects
Today, I’m extremely happy to announce Amazon SageMaker Pipelines, a new capability of Amazon SageMaker that makes it easy for data scientists and engineers to build, automate, and scale end to end machine learning pipelines. Machine learning (ML) is intrinsically experimental and unpredictable in nature. You spend days or weeks exploring and processing data in […]
Introducing Amazon SageMaker Data Wrangler, a Visual Interface to Prepare Data for Machine Learning
Today, I’m extremely happy to announce Amazon SageMaker Data Wrangler, a new capability of Amazon SageMaker that makes it faster for data scientists and engineers to prepare data for machine learning (ML) applications by using a visual interface. Whenever I ask a group of data scientists and ML engineers how much time they actually spend […]
Preview: Amazon Lookout for Metrics, an Anomaly Detection Service for Monitoring the Health of Your Business
We are excited to announce Amazon Lookout for Metrics, a new service that uses machine learning (ML) to detect anomalies in your metrics, helping you proactively monitor the health of your business, diagnose issues, and find opportunities quickly – with no ML experience required. Lookout for Metrics uses the same technology used by Amazon to […]
New – Amazon SageMaker Clarify Detects Bias and Increases the Transparency of Machine Learning Models
Today, I’m extremely happy to announce Amazon SageMaker Clarify, a new capability of Amazon SageMaker that helps customers detect bias in machine learning (ML) models, and increase transparency by helping explain model behavior to stakeholders and customers. As ML models are built by training algorithms that learn statistical patterns present in datasets, several questions immediately […]
New – Profile Your Machine Learning Training Jobs With Amazon SageMaker Debugger
Today, I’m extremely happy to announce that Amazon SageMaker Debugger can now profile machine learning models, making it much easier to identify and fix training issues caused by hardware resource usage. Despite its impressive performance on a wide range of business problems, machine learning (ML) remains a bit of a mysterious topic. Getting things right […]
New – Data Parallelism Library in Amazon SageMaker Simplifies Training on Large Datasets
Today, I’m particularly happy to announce that Amazon SageMaker now supports a new data parallelism library that makes it easier to train models on datasets that may be as large as hundreds or thousands of gigabytes. As data sets and models grow larger and more sophisticated, machine learning (ML) practitioners working on large distributed training […]
re:Invent 2020 Liveblog: Machine Learning Keynote
AWS Chief Evangelist Jeff Barr and Developer Advocates Martin Beeby and Steve Roberts liveblogged the first-ever Machine Learning Keynote. Swami Sivasubramanian, VP of Amazon Machine Learning shared the latest developments and launches in Amazon ML/AI, as well as demos of new technology, and insights from customers. Read the Machine Learning Keynote recap below. You can […]
New – SaaS Lens in AWS Well-Architected Tool
To help you build secure, high-performing, resilient, and efficient solutions on AWS, in 2015 we publicly launched the AWS Well-Architected Framework. It started as a single whitepaper but has expanded to include domain-specific lenses, hands-on labs, and the AWS Well-Architected Tool (available at no cost in the AWS Management Console) that provides a mechanism for […]