AWS News Blog

Category: Amazon SageMaker

Scaling Ad Verification with Machine Learning and AWS Inferentia

Amazon Advertising helps companies build their brand and connect with shoppers, through ads shown both within and beyond Amazon’s store, including websites, apps, and streaming TV content in more than 15 countries. Businesses or brands of all sizes including registered sellers, vendors, book vendors, Kindle Direct Publishing (KDP) authors, app developers, and agencies on Amazon […]

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Amazon SageMaker Named as the Outright Leader in Enterprise MLOps Platforms

Over the last few years, Machine Learning (ML) has proven its worth in helping organizations increase efficiency and foster innovation. As ML matures, the focus naturally shifts from experimentation to production. ML processes need to be streamlined, standardized, and automated to build, train, deploy, and manage models in a consistent and reliable way. Perennial IT […]

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Architectural diagram.

Amazon Redshift ML Is Now Generally Available – Use SQL to Create Machine Learning Models and Make Predictions from Your Data

With Amazon Redshift, you can use SQL to query and combine exabytes of structured and semi-structured data across your data warehouse, operational databases, and data lake. Now that AQUA (Advanced Query Accelerator) is generally available, you can improve the performance of your queries by up to 10 times with no additional costs and no code […]

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Decrease Your Machine Learning Costs with Instance Price Reductions and Savings Plans for Amazon SageMaker

Launched at AWS re:Invent 2017, Amazon SageMaker is a fully-managed service that has already helped tens of thousands of customers quickly build and deploy their machine learning (ML) workflows on AWS. To help them get the most ML bang for their buck, we’ve added a string of cost-optimization services and capabilities, such as Managed Spot […]

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Amazon SageMaker JumpStart Simplifies Access to Pre-built Models and Machine Learning Solutions

Today, I’m extremely happy to announce the availability of Amazon SageMaker JumpStart, a capability of Amazon SageMaker that accelerates your machine learning workflows with one-click access to popular model collections (also known as “model zoos”), and to end-to-end solutions that solve common use cases. In recent years, machine learning (ML) has proven to be a […]

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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 […]

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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 […]

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New – Store, Discover, and Share Machine Learning Features with Amazon SageMaker Feature Store

Today, I’m extremely happy to announce Amazon SageMaker Feature Store, a new capability of Amazon SageMaker that makes it easy for data scientists and machine learning engineers to securely store, discover and share curated data used in training and prediction workflows. For all the importance of selecting the right algorithm to train machine learning (ML) […]

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