For model building, Amazon SageMaker lets you build, train, and deploy machine learning and deep learning models; SageMaker Studio is based on the popular JupyterLab web-based development environment.

We called it Machine Learning October Fest. Last week saw the nearly synchronized breakout of a number of news centered around machine learning (ML): The release of PyTorch 1.0 beta from Facebook ...

Falkonry Launches Industry’s First Machine Learning Product Enabling Non-Data Scientists to Create and Deploy Predictive Operations in Cloud, on Premises, and at the Edge ...

They can be up to 100 times smaller, he went on to add, and the goal is to be able to deploy the same machine learning model in the data center and at the edge.

The newly-open sourced Distributed Machine Learning Toolkit features fast, parallelized, and easy-to-deploy machine learning algorithms Topics Spotlight: New Thinking about Cloud Computing ...

At Cloud Next 2019, Google announced the launch of AI Platform, a comprehensive machine learning service for developers and data scientists. Google has many investments in the space of machine ...

He says that a data scientist starts by uploading an exported model file to S3 cloud storage. “Then we pull it, containerize it and deploy it on Kubernetes behind the scenes.

Iterative has launched Machine Learning Engineering Management an open source model deployment and registry tool. MLOps Company Iterative Introduces the First Open, Git-based Machine Learning ...

While teams spend lots of energy developing a machine learning model, it’s hard to actually deploy the model for customers to use. That’s where Graphpipe comes in.