AWS Machine Learning Blog
Category: Compute
How Sportradar used the Deep Java Library to build production-scale ML platforms for increased performance and efficiency
This is a guest post co-written with Fred Wu from Sportradar. Sportradar is the world’s leading sports technology company, at the intersection between sports, media, and betting. More than 1,700 sports federations, media outlets, betting operators, and consumer platforms across 120 countries rely on Sportradar knowhow and technology to boost their business. Sportradar uses data […]
Modulate makes voice chat safer while reducing infrastructure costs by a factor of 5 with Amazon EC2 G5g instances
This is a guest post by Carter Huffman, CTO and Co-founder at Modulate. Modulate is a Boston-based startup on a mission to build richer, safer, more inclusive online gaming experiences for everyone. We’re a team of world-class audio experts, gamers, allies, and futurists who are eager to build a better online world and make voice […]
Deploy pre-trained models on AWS Wavelength with 5G edge using Amazon SageMaker JumpStart
With the advent of high-speed 5G mobile networks, enterprises are more easily positioned than ever with the opportunity to harness the convergence of telecommunications networks and the cloud. As one of the most prominent use cases to date, machine learning (ML) at the edge has allowed enterprises to deploy ML models closer to their end-customers […]
Real-time fraud detection using AWS serverless and machine learning services
Online fraud has a widespread impact on businesses and requires an effective end-to-end strategy to detect and prevent new account fraud and account takeovers, and stop suspicious payment transactions. In this post, we show a serverless approach to detect online transaction fraud in near-real time. We show how you can apply this approach to various data streaming and event-driven architectures, depending on the desired outcome and actions to take to prevent fraud (such as alert the user about the fraud or flag the transaction for additional review).
Use Snowflake as a data source to train ML models with Amazon SageMaker
May 2023: This blog post has been updated to include a workflow that does not require building a custom container. Amazon SageMaker is a fully managed machine learning (ML) service. With SageMaker, data scientists and developers can quickly and easily build and train ML models, and then directly deploy them into a production-ready hosted environment. […]
How Marubeni is optimizing market decisions using AWS machine learning and analytics
This post is co-authored with Hernan Figueroa, Sr. Manager Data Science at Marubeni Power International. Marubeni Power International Inc (MPII) owns and invests in power business platforms in the Americas. An important vertical for MPII is asset management for renewable energy and energy storage assets, which are critical to reduce the carbon intensity of our […]
Accelerate hyperparameter grid search for sentiment analysis with BERT models using Weights & Biases, Amazon EKS, and TorchElastic
Financial market participants are faced with an overload of information that influences their decisions, and sentiment analysis stands out as a useful tool to help separate out the relevant and meaningful facts and figures. However, the same piece of news can have a positive or negative impact on stock prices, which presents a challenge for […]
Scaling Large Language Model (LLM) training with Amazon EC2 Trn1 UltraClusters
Modern model pre-training often calls for larger cluster deployment to reduce time and cost. At the server level, such training workloads demand faster compute and increased memory allocation. As models grow to hundreds of billions of parameters, they require a distributed training mechanism that spans multiple nodes (instances). In October 2022, we launched Amazon EC2 […]
Scaling distributed training with AWS Trainium and Amazon EKS
Recent developments in deep learning have led to increasingly large models such as GPT-3, BLOOM, and OPT, some of which are already in excess of 100 billion parameters. Although larger models tend to be more powerful, training such models requires significant computational resources. Even with the use of advanced distributed training libraries like FSDP and […]
Deploy a machine learning inference data capture solution on AWS Lambda
Monitoring machine learning (ML) predictions can help improve the quality of deployed models. Capturing the data from inferences made in production can enable you to monitor your deployed models and detect deviations in model quality. Early and proactive detection of these deviations enables you to take corrective actions, such as retraining models, auditing upstream systems, […]