AWS Machine Learning Blog

Category: Artificial Intelligence

Using the AWS DeepRacer new Soft Actor Critic algorithm with continuous action spaces

AWS DeepRacer is the fastest way to get started with machine learning (ML). You can train reinforcement learning (RL) models by using a 1/18th scale autonomous vehicle in a cloud-based virtual simulator and compete for prizes and glory in the global AWS DeepRacer League. We’re excited to bring you two new features available on the […]

Read More

Scheduling work meetings in Slack with Amazon Lex

Imagine being able to schedule a meeting or get notified about updates in your code repositories without leaving your preferred messaging platform. This could save you time and increase productivity. With the advent of chatbots, these mundane tasks are now easier than ever. Amazon Lex, a service for building chatbots, offers native integration with popular […]

Read More

Automating complex deep learning model training using Amazon SageMaker Debugger and AWS Step Functions

Amazon SageMaker Debugger can monitor ML model parameters, metrics, and computation resources as the model optimization is in progress. You can use it to identify issues during training, gain insights, and take actions like stopping the training or sending notifications through built-in or custom actions. Debugger is particularly useful in training challenging deep learning model […]

Read More
The following diagram shows the serverless architecture that you build.

Setting up an IVR to collect customer feedback via phone using Amazon Connect and AWS AI Services

As many companies place their focus on customer centricity, customer feedback becomes a top priority. However, as new laws are formed, for instance GDPR in Europe, collecting feedback from customers can become increasingly difficult. One means of collecting this feedback is via phone. When a customer calls an agency or call center, feedback may be […]

Read More

This month in AWS Machine Learning: January edition

Hello and welcome to our first “This month in AWS Machine Learning” of 2021! Every day there is something new going on in the world of AWS Machine Learning—from launches to new to use cases to interactive trainings. We’re packaging some of the not-to-miss information from the ML Blog and beyond for easy perusing each […]

Read More

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 […]

Read More
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 […]

Read More

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 […]

Read More

Managing your machine learning lifecycle with MLflow and Amazon SageMaker

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 initial phase of an ML project, data scientists collaborate and share experiment results in order to find a solution to a business need. During the operational phase, you also need […]

Read More
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

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 can spend up to 60-70% of their time on feature […]

Read More