Amazon SageMaker is a fully-managed service that provides every developer and data scientist with the ability to quickly build, train, and deploy machine learning (ML) models at scale. ML is realized in inference. SageMaker offers four …
Read More
Suppose you are trying to solve a problem of interest (e.g., portfolio optimization or machine learning), and you are given some classical data, neatly arranged into a matrix A. Your plan is to solve this problem using a quantum algorithm …
Read More
Deep learning (DL) models have been increasing in size and complexity over the last few years, pushing the time to train from days to weeks. Training large language models the size of GPT-3 can take months, leading to an exponential growth …
Read More
Amazon’s product search engine indexes billions of products, serves hundreds of millions of customers worldwide, and is one of the most heavily used services in the world. The Amazon Search team develops machine learning (ML) technology …
Read More
After data scientists carefully come up with a satisfying machine learning (ML) model, the model must be deployed to be easily accessible for inference by other members of the organization. However, deploying models at scale with optimized …
Read More
Ampersand is a data-driven TV advertising technology company that provides aggregated TV audience impression insights and planning on 42 million households, in every media market, across more than 165 networks and apps and in all dayparts …
Read More
The AWS Graviton 3 will be launched this year as part of the new C7g instance. Inside this chip are some interesting innovations that already have a lot of HPC and AI/ML customers interested. We sat down with Olly Perks, who recently joined …
Read More
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 …
Read More