I believe an approach to machine learning deployment that’s based on an industry standard, language-agnostic, and able to represent a broad range of algorithms is the clear path forward.

There are a lot of considerations and factors to manage when deploying a machine learning model – or a fleet of machine learning models. Six Nines is glad to help by providing resources, and ...

Learn how to organize and structure your machine learning projects for real-world deployment. From directory layout to model ...

While building machine learning models is fundamental to today’s narrow applications of AI, there are a variety of different ways to go about realizing the same ends. So-called machine learning ...

Offers advanced AI-driven predictive modeling, data preparation, and automation tools for enterprises seeking scalable ...

According to Ashley Kramer, Alteryx's VP of Product Management, Promote will address this gap by allowing deployment of models, and generation of REST APIs around them, all of which can be invoked ...

The 1.3 release of MCenter specifically addresses the deployment challenges of machine learning for real-time, production applications. According to ParallelM, many existing data science tools with ...

Teams are then able build a model registry in hours rather than days. MLEM promotes a comprehensive machine learning model lifecycle management workflow using a GitOps-based approach.

MLOps platform Iterative, which announced a $20 million Series A round almost exactly a year ago, today launched MLEM, an open source Git-based machine learning model management and deployment tool.