Category: Analytics

One of the challenges encountered by teams using Amazon Lookout for Metrics is quickly and efficiently connecting it to data visualization. The anomalies are presented individually on the Lookout for Metrics console, each with their own graph, making it difficult to view the set as a whole. An automated, integrated solution is needed for deeper […]

This is the second part of a series that showcases the machine learning (ML) lifecycle with a data mesh design pattern for a large enterprise with multiple lines of business (LOBs) and a Center of Excellence (CoE) for analytics and ML. In part 1, we addressed the data steward persona and showcased a data mesh […]

Organizations are increasingly building and using machine learning (ML)-powered solutions for a variety of use cases and problems, including predictive maintenance of machine parts, product recommendations based on customer preferences, credit profiling, content moderation, fraud detection, and more. In many of these scenarios, the effectiveness and benefits derived from these ML-powered solutions can be further […]

August 30, 2023: Amazon Kinesis Data Analytics has been renamed to Amazon Managed Service for Apache Flink. Read the announcement in the AWS News Blog and learn more. You may have applications that generate streaming data that is full of records containing customer case notes, product reviews, and social media messages, in many languages. Your […]

This blog post is co-written by Jonathan Lee, Nelson Leung, Paul Min, and Troy Squillaci from Intel.  In Part 1 of this post, we discussed how Intel®3DAT collaborated with AWS Machine Learning Professional Services (MLPS) to build a scalable AI SaaS application. 3DAT uses computer vision and AI to recognize, track, and analyze over 1,000 […]

This post was last reviewed and updated June, 2022 with revised feature groups (tables) and features (columns) permissions. You can establish feature stores to provide a central repository for machine learning (ML) features that can be shared with data science teams across your organization for training, batch scoring, and real-time inference. Data science teams can […]

Amazon Rekognition Custom Labels is a fully managed computer vision service that allows developers to build custom models to classify and identify objects in images that are specific and unique to your business. Rekognition Custom Labels doesn’t require you to have any prior computer vision expertise. You can get started by simply uploading tens of […]

February 9, 2024: Amazon Kinesis Data Firehose has been renamed to Amazon Data Firehose. Read the AWS What’s New post to learn more. Amazon SageMaker Autopilot makes it possible for organizations to quickly build and deploy an end-to-end machine learning (ML) model and inference pipeline with just a few lines of code or even without […]

Customers in many different domains tend to work with multiple sources for their data: object-based storage like Amazon Simple Storage Service (Amazon S3), relational databases like Amazon Relational Database Service (Amazon RDS), or data warehouses like Amazon Redshift. Machine learning (ML) practitioners are often driven to work with objects and files instead of databases and […]

July 2024: This post was reviewed and updated for accuracy. Amazon Redshift, a fast, fully managed, widely used cloud data warehouse, natively integrates with Amazon SageMaker for machine learning (ML). Tens of thousands of customers use Amazon Redshift to process exabytes of data every day to power their analytics workloads. Data analysts and database developers […]