AWS Machine Learning Blog

Category: Artificial Intelligence

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 […]

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 […]

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 […]

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, […]

Try asking OSU-OKC bot, OKC Pete, some questions of your own via the university website.

Building an omnichannel Q&A chatbot with Amazon Connect, Amazon Lex, Amazon Kendra, and the open-source QnABot project

For many students, embarking on a higher education journey is an exciting time filled with new experiences. However, like anything new, it also can also bring plenty of questions to answer and obstacles to overcome. Oklahoma State University, Oklahoma City (OSU-OKC) recognized this, and was intent on providing a better solution to address student questions […]

Data processing options for AI/ML

This blog post was reviewed and updated June, 2022 to include new features that have been added to the Data processing such as Amazon SageMaker Studio and EMR integration. Training an accurate machine learning (ML) model requires many different steps, but none are potentially more important than data processing. Examples of processing steps include converting […]

The following diagram illustrates this architecture.

Translating JSON documents using Amazon Translate

September 2021: This post and the solution has been updated to use the Amazon EventBridge events notifications in Amazon Translate for tracking Amazon Translate Batch Translation job completion. JavaScript Object Notation (JSON) is a schema-less, lightweight format for storing and transporting data. It’s a text-based, self-describing representation of structured data that is based on key-value […]

Using container images to run PyTorch models in AWS Lambda

PyTorch is an open-source machine learning (ML) library widely used to develop neural networks and ML models. Those models are usually trained on multiple GPU instances to speed up training, resulting in expensive training time and model sizes up to a few gigabytes. After they’re trained, these models are deployed in production to produce inferences. […]

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 […]

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 […]