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At its I/O developers conference, Google today announced its new ML Hub, a one-stop destination for developers who want to get more guidance on how to train and deploy their ML models, no matter ...
This article is part of a VB special issue. Read the full series here: The quest for Nirvana: Applying AI at scale. To say that it’s challenging to achieve AI at scale across the enterprise would be ...
Snowpark ML Operations Enhancements: The Snowpark Model Registry (public preview soon) now builds on a native Snowflake model entity and enables the scalable, secure deployment and management of ...
Dotscience, a pioneer in DevOps for Machine Learning (MLOps), announced new platform advancements that offer the easiest way to deploy and monitor ML models on Kubernetes clusters, making Kubernetes ...
The ability to run large language models (LLMs), such as Deepseek, directly on mobile devices is reshaping the AI landscape. By allowing local inference, you can minimize reliance on cloud ...
Today, nearly any company – even enterprises with large teams of sophisticated data scientists and engineers – can be brought to heel by machine learning (ML) models that fail spectacularly in the ...
OctoML, the well-funded machine learning startup that helps enterprises optimize and deploy their models, launched a major update to its product today that will make it far easier for developers to ...
Snowflake Notebooks unlock data exploration and machine learning development for SQL and Python users with an interactive, cell-based programming environment Snowflake advances Snowpark to streamline ...
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