Category: Amazon Elastic Kubernetes Service

This post is co-written with Chaoyang He, Al Nevarez and Salman Avestimehr from FedML. Many organizations are implementing machine learning (ML) to enhance their business decision-making through automation and the use of large distributed datasets. With increased access to data, ML has the potential to provide unparalleled business insights and opportunities. However, the sharing of […]

This post details how to set up container-based GPU metrics and provides an example of collecting these metrics from EKS pods.

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 […]

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 […]

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 […]

This post was co-written with Sachin Kadyan, a leading developer of OpenFold. In drug discovery, understanding the 3D structure of proteins is key to assessing the ability of a drug to bind to it, directly impacting its efficacy. Predicting the 3D protein form, however, is very complex, challenging, expensive, and time consuming, and can take […]

In this post, we demonstrate how Kubeflow on AWS (an AWS-specific distribution of Kubeflow) used with AWS Deep Learning Containers and Amazon Elastic File System (Amazon EFS) simplifies collaboration and provides flexibility in training deep learning models at scale on both Amazon Elastic Kubernetes Service (Amazon EKS) and Amazon SageMaker utilizing a hybrid architecture approach. […]

Distributed deep learning model training is becoming increasingly important as data sizes are growing in many industries. Many applications in computer vision and natural language processing now require training of deep learning models, which are growing exponentially in complexity and are often trained with hundreds of terabytes of data. It then becomes important to use […]

This blog post is co-written by Jonathan Lee, Nelson Leung, Paul Min, and Troy Squillaci from Intel.  In Part 1 of this post, we discussed how Intel®3DAT collaborated with AWS Machine Learning Professional Services (MLPS) to build a scalable AI SaaS application. 3DAT uses computer vision and AI to recognize, track, and analyze over 1,000 […]

In this post, we demonstrate Kubeflow on AWS (an AWS-specific distribution of Kubeflow) and the value it adds over open-source Kubeflow through the integration of highly optimized, cloud-native, enterprise-ready AWS services. Kubeflow is the open-source machine learning (ML) platform dedicated to making deployments of ML workflows on Kubernetes simple, portable and scalable. Kubeflow provides many […]