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MLOps or ML Ops is a paradigm that aims to deploy and maintain machine learning models in production reliably and efficiently. The word is a compound of "machine learning" and the continuous delivery practice (CI/CD) of DevOps in the software field.
MLOps (machine learning operations) is a discipline that enables data scientists and IT professionals to collaborate and communicate while automating machine learning algorithms. It extends and expands on the principles of DevOps to support the automation of developing and deploying machine learning models and applications. [ 13 ]
DVC is a free and open-source, platform-agnostic version system for data, machine learning models, and experiments. [1] It is designed to make ML models shareable, experiments reproducible, [2] and to track versions of models, data, and pipelines.
Seldon Technologies Limited (commonly referred to as Seldon) is a British technology company founded in 2014, and headquartered in London, England.It makes MLOps software for enterprise deployment of machine learning models, and is a primary maintainer and contributor to a number of popular open source repositories such as Seldon Core.
Automated machine learning (AutoML) is the process of automating the tasks of applying machine learning to real-world problems. It is the combination of automation and ML. [1]
Azure was once again the primary focus when Microsoft reported its earnings results. The unit grew its revenue by 31% in the fiscal second quarter, which was a deceleration from Q1 when Azure ...
If you've been having trouble with any of the connections or words in Tuesday's puzzle, you're not alone and these hints should definitely help you out. Plus, I'll reveal the answers further down ...
Azure Maps Creator is a tool for generating custom maps for locations like large office complexes, construction sites, or university campuses. These maps can then be integrated into applications and used with other Azure Maps functions for purposes such as wayfinding and maintenance and security in building automation contexts. [64]