AWS Machine Learning Blog

Category: Amazon SageMaker

This dataset contains 500 images of bees that have been uploaded by iNaturalist users for the purposes of recording the observation and identification.

Training and deploying models using TensorFlow 2 with the Object Detection API on Amazon SageMaker

With the rapid growth of object detection techniques, several frameworks with packaged pre-trained models have been developed to provide users easy access to transfer learning. For example, GluonCV, Detectron2, and the TensorFlow Object Detection API are three popular computer vision frameworks with pre-trained models. In this post, we use Amazon SageMaker to build, train, and […]

Read More
The following diagram illustrates the overall architecture of this approach.

Using genetic algorithms on AWS for optimization problems

Machine learning (ML)-based solutions are capable of solving complex problems, from voice recognition to finding and identifying faces in video clips or photographs. Usually, these solutions use large amounts of training data, which results in a model that processes input data and produces numeric output that can be interpreted as a word, face, or classification […]

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

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
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

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

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 […]

Read More
The following diagram illustrates the main steps you need to complete in order to create and publish your custom SageMaker project template.

Multi-account model deployment with Amazon SageMaker Pipelines

Amazon SageMaker Pipelines is the first purpose-built CI/CD service for machine learning (ML). It helps you build, automate, manage, and scale end-to-end ML workflows and apply DevOps best practices of CI/CD to ML (also known as MLOps). Creating multiple accounts to organize all the resources of your organization is a good DevOps practice. A multi-account […]

Read More
The following screenshot shows how the three components of SageMaker Pipelines can work together in an example SageMaker project.

Building, automating, managing, and scaling ML workflows using Amazon SageMaker Pipelines

We recently announced Amazon SageMaker Pipelines, the first purpose-built, easy-to-use continuous integration and continuous delivery (CI/CD) service for machine learning (ML). SageMaker Pipelines is a native workflow orchestration tool for building ML pipelines that take advantage of direct Amazon SageMaker integration. Three components improve the operational resilience and reproducibility of your ML workflows: pipelines, model […]

Read More