AWS Machine Learning Blog

Category: Amazon SageMaker

The following diagram shows our end-to-end automated MLOps pipeline

Architect and build the full machine learning lifecycle with AWS: An end-to-end Amazon SageMaker demo

In this tutorial, we will walk through the entire machine learning (ML) lifecycle and show you how to architect and build an ML use case end to end using Amazon SageMaker. Amazon SageMaker provides a rich set of capabilities that enable data scientists, machine learning engineers, and developers to prepare, build, train, and deploy ML […]

Read More

How Zopa enhanced their fraud detection application using Amazon SageMaker Clarify

This post is co-authored by Jiahang Zhong, Head of Data Science at Zopa.  Zopa is a UK-based digital bank and peer to peer (P2P) lender. In 2005, Zopa launched the first ever P2P lending company to give people access to simpler, better-value loans and investments. In 2020, Zopa received a full bank license to offer […]

Read More
The blue line in the following forecasted plot represents the historical energy usage for a specific client.

Training, debugging and running time series forecasting models with the GluonTS toolkit on Amazon SageMaker

Time series forecasting is an approach to predict future data values by analyzing the patterns and trends in past observations over time. Organizations across industries require time series forecasting for a variety of use cases, including seasonal sales prediction, demand forecasting, stock price forecasting, weather forecasting, financial planning, and inventory planning. Various cutting edge algorithms […]

Read More
The following is the architecture diagram for integrating online ML inference in a telemedicine contact flow via Amazon Connect.

Applying voice classification in an Amazon Connect telemedicine contact flow

Given the rising demand for fast and effective COVID-19 detection, customers are exploring the usage of respiratory sound data, like coughing, breathing, and counting, to automatically diagnose COVID-19 based on machine learning (ML) models. University of Cambridge researchers built a COVID-19 sound application and demonstrated that a simple binary ML classifier can classify healthy and […]

Read More
The following diagram illustrates the solution architecture.

Machine learning on distributed Dask using Amazon SageMaker and AWS Fargate

As businesses around the world are embarking on building innovative solutions, we’re seeing a growing trend adopting data science workloads across various industries. Recently, we’ve seen a greater push towards reducing the friction between data engineers and data scientists. Data scientists are now enabled to run their experiments on their local machine and port to […]

Read More
Schematically, this process looks like the following diagram.

Solving numerical optimization problems like scheduling, routing, and allocation with Amazon SageMaker Processing

In this post, we discuss solving numerical optimization problems using the very flexible Amazon SageMaker Processing API. Optimization is the process of finding the minimum (or maximum) of a function that depends on some inputs, called design variables. This pattern is relevant to solving business-critical problems such as scheduling, routing, allocation, shape optimization, trajectory optimization, […]

Read More
The following diagram is the architecture for the secure environment developed in this workshop.

Building secure machine learning environments with Amazon SageMaker

As businesses and IT leaders look to accelerate the adoption of machine learning (ML) and artificial intelligence (AI), there is a growing need to understand how to build secure and compliant ML environments that meet enterprise requirements. One major challenge you may face is integrating ML workflows into existing IT and business work streams. A […]

Read More

Running multiple HPO jobs in parallel on Amazon SageMaker

The ability to rapidly iterate and train machine learning (ML) models is key to deriving business value from ML workloads. Because ML models often have many tunable parameters (known as hyperparameters) that can influence the model’s ability to effectively learn, data scientists often use a technique known as hyperparameter optimization (HPO) to achieve the best-performing […]

Read More
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