Why you need a GitOps-based Model Registry - Dmitry Petrov - Ai4 2022 скачать в хорошем качестве

Why you need a GitOps-based Model Registry - Dmitry Petrov - Ai4 2022

Model registries are a key tool in addressing challenges around the ML lifecycle of models. They allow you to register, version, and manage models and their associated information throughout the deployment lifecycle. This session will go over MLOps challenges solved by model registry, what core requirements your team should think about when implementing one, and why a GitOps-based approach leads to the fastest time-to-market delivery of your ML models into production apps and services. In this MLOps session from Ai4 Conference 2022, Dmitry Petrov teaches: ● What an ML model registry is and what problems it solves ● What considerations to have when implementing a model registry ● Why a Git-based model registry will make both your MLOps and DevOps teams happy Dmitry Petrov Bio Dmitry Petrov is an ex-Data Scientist at Microsoft with Ph.D. in Computer Science and active open source contributor. He has written and open sourced the first version of DVC.org – machine learning workflow management tool. Also he implemented Wavelet-based image hashing algorithm (wHash) in open source library ImageHash for Python. Now Dmitry is working on tools for machine learning and ML workflow management as a co-founder and CEO of Iterative in San Francisco. To learn more about Iterative's open-source and SaaS tools please visit: 🧑🏽‍💻 Our online course: https://learn.iterative.ai ✍🏼 Our docs: https://dvc.org/doc https://cml.dev/doc https://mlem.ai/doc https://studio.iterative.ai