AWS Machine Learning Blog
Category: Artificial Intelligence
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 […]
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 […]
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 […]
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, […]
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 […]
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. […]
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 […]