Continuous Delivery for Machine Learning (CD4ML)

Introduction to ML OpsПодробнее

CD4ML: Continous Delivery for Machine Learning - Eduardo Santos [PyBR2022]Подробнее

Building Robust ML Production Systems Using OSS Tools for Continuous Delivery... Dr. Fabio M. GrätzПодробнее

MLOps Simple explanation | Building scalable machine learning pipelines | #shorts #machinelearningПодробнее

Explainable Artificial Intelligence and Continuous Delivery: Towards the intelligent organizationПодробнее

Merantix | Parameterizing and Executing Flyte Workflows With Hydra-CoreПодробнее

Continuous Delivery for Machine Learning article by Martin Fowler // Coffee Session short clip #53Подробнее

Continuous Delivery for Machine Learning article by Martin Fowler // Coffee Session short clip #53Подробнее

Cultura Data-Driven com Danilo SatoПодробнее

Getting Machine Learning applications into production using MLOps and CD4ML – Eric Nagler – XConf NAПодробнее

SEMLA 2021 - Continuous Delivery for Machine LearningПодробнее

End-To-End Operationalization Of ML.NET Models - Alexander Slotte @ Philly.NET Code Camp 2021Подробнее

DataOpsDC 1/26/2021 - MLOps - End-to-End Operationalization of Machine Learning AlgorithmsПодробнее

Deep Learning’s Most Important Ideas | Machine Learning Monthly November 2020Подробнее

Webinar: Entrega Continua para modelos de Machine Learning (CD4ML)Подробнее

TensorFlow London: CD4ML and the challenges of testing and quality in ML systemsПодробнее

Tech Radar Vol. 22 Sneak Peek WebinarПодробнее

Stepan Maksymchuk - CD4MLПодробнее

Continuous Delivery For Machine Learning: Patterns And Pains - Emily GorcenskiПодробнее

AWS re:Invent 2019: Continuous deployment for ML: The new software development lifecycle (DEM30-S)Подробнее