AWS Machine Learning Blog

Get ready to roll! AWS DeepRacer pre-season racing is now open

AWS DeepRacer allows you to get hands on with machine learning (ML) through a fully autonomous 1/18th scale race car driven by reinforcement learning, a 3D racing simulator on the AWS DeepRacer console, a global racing league, and hundreds of customer-initiated community races. Pre-season qualifying underway We’re excited to announce that racing action is right […]

In this post, we implement the area in red of the following architecture.

Performing anomaly detection on industrial equipment using audio signals

Industrial companies have been collecting a massive amount of time-series data about operating processes, manufacturing production lines, and industrial equipment. You might store years of data in historian systems or in your factory information system at large. Whether you’re looking to prevent equipment breakdown that would stop a production line, avoid catastrophic failures in a […]

Forecasting AWS spend using the AWS Cost and Usage Reports, AWS Glue DataBrew, and Amazon Forecast

AWS Cost Explorer enables you to view and analyze your AWS Cost and Usage Reports (AWS CUR). You can also predict your overall cost associated with AWS services in the future by creating a forecast of AWS Cost Explorer, but you can’t view historical data beyond 12 months. Moreover, running custom machine learning (ML) models […]

Managing your machine learning lifecycle with MLflow and Amazon SageMaker

June 2024: The contents of this post are out of date. We recommend you refer to Announcing the general availability of fully managed MLflow on Amazon SageMaker for the latest. With the rapid adoption of machine learning (ML) and MLOps, enterprises want to increase the velocity of ML projects from experimentation to production. During the […]

The following diagram illustrates some of the services that can be integrated with SageMaker Feature Store.

Understanding the key capabilities of Amazon SageMaker Feature Store

October 2022: This post was reviewed and updated for accuracy. One of the challenging parts of machine learning (ML) is feature engineering, the process of transforming data to create features for ML. Features are processed data signals used for training ML models and for deployed models to make accurate predictions. Data scientists and ML engineers […]

Saving time with personalized videos using AWS machine learning

CLIPr aspires to help save 1 billion hours of people’s time. We organize video into a first-class, searchable data source that unlocks the content most relevant to your interests using AWS machine learning (ML) services. CLIPr simplifies the extraction of information in videos, saving you hours by eliminating the need to skim through them manually […]

The first screenshot shows the profile while running with DP.

Deepset achieves a 3.9x speedup and 12.8x cost reduction for training NLP models by working with AWS and NVIDIA

This is a guest post from deepset (creators of the open source frameworks FARM and Haystack), and was contributed to by authors from NVIDIA and AWS.  At deepset, we’re building the next-level search engine for business documents. Our core product, Haystack, is an open-source framework that enables developers to utilize the latest NLP models for […]

How to deliver natural conversational experiences using Amazon Lex Streaming APIs

Natural conversations often include pauses and interruptions. During customer service calls, a caller may ask to pause the conversation or hold the line while they look up the necessary information before continuing to answer a question. For example, callers often need time to retrieve credit card details when making bill payments. Interruptions are also common. […]

Model serving in Java with AWS Elastic Beanstalk made easy with Deep Java Library

Deploying your machine learning (ML) models to run on a REST endpoint has never been easier. Using AWS Elastic Beanstalk and Amazon Elastic Compute Cloud (Amazon EC2) to host your endpoint and Deep Java Library (DJL) to load your deep learning models for inference makes the model deployment process extremely easy to set up. Setting […]

We can improve the accuracy by retraining the model with more video files.

Building your own brand detection and visibility using Amazon SageMaker Ground Truth and Amazon Rekognition Custom Labels – Part 1: End-to-end solution

According to Gartner, 58% of marketing leaders believe brand is a critical driver of buyer behavior for prospects, and 65% believe it’s a critical driver of buyer behavior for existing customers. Companies spend huge amounts of money on advertisement to raise brand visibility and awareness. In fact, as per Gartner, CMO spends over 21% of […]