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Machine Learning Operations (MLOPs) with Azure Machine Learning

ODSC - Open Data Science

Machine Learning Operations (MLOps) can significantly accelerate how data scientists and ML engineers meet organizational needs. A well-implemented MLOps process not only expedites the transition from testing to production but also offers ownership, lineage, and historical data about ML artifacts used within the team.

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MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

Collaborative workflows : Dataset storage and versioning tools should support collaborative workflows, allowing multiple users to access and contribute to datasets simultaneously, ensuring efficient collaboration among ML engineers, data scientists, and other stakeholders.

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Operationalize LLM Evaluation at Scale using Amazon SageMaker Clarify and MLOps services

AWS Machine Learning Blog

An evaluation is a task used to measure the quality and responsibility of output of an LLM or generative AI service. He has spent 15+ years on inventing, designing, leading, and implementing innovative end-to-end production-level ML and AI solutions in the domains of energy, retail, health, finance, motorsports etc.

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Definite Guide to Building a Machine Learning Platform

The MLOps Blog

As the number of ML-powered apps and services grows, it gets overwhelming for data scientists and ML engineers to build and deploy models at scale. In this comprehensive guide, we’ll explore everything you need to know about machine learning platforms, including: Components that make up an ML platform.