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The idea here, the company says, is to bridge the gap between ML engineers and DevOps teams by using the git-based approach that developers are already familiar with. Using MLEM, developers can store and track their ML models throughout their lifecycle. As such, it complements Iterative open source GTO artifact registry and DVC, the company version control system for data and models. Having a machine learning [url=https://www.nikeairforce.es]air af1[/url] model registry is becoming an essential part of the machine learning technology stack. Current SaaS solutions can lead to a divergen [url=https://www.nike-airjordan.it]air jordan4[/url] ce in the lifecycle of ML models and software [url=https://www.nikedunk.us]nike dunks women[/url] applications, said Dmitry Petrov, co-founder and CEO of Iterative. Our approach to an ML model registry is to provide modular building blocks that organizations can easily integrate into their existing MLOps tech stack. MLEM is used for extracting meta-information for ML models and simplifying deployment. DVC manages large ML model files in cloud or on-prem storage. GTO Fdum October lets 11 public companies borrow money on its platform
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