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Improving air quality with generative AI

AWS Machine Learning Blog

This manual synchronization process, hindered by disparate data formats, is resource-intensive, limiting the potential for widespread data orchestration. The platform, although functional, deals with CSV and JSON files containing hundreds of thousands of rows from various manufacturers, demanding substantial effort for data ingestion.

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Machine Learning Operations (MLOPs) with Azure Machine Learning

ODSC - Open Data Science

Data Estate: This element represents the organizational data estate, potential data sources, and targets for a data science project. Data Engineers would be the primary owners of this element of the MLOps v2 lifecycle. The Azure data platforms in this diagram are neither exhaustive nor prescriptive.

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Definite Guide to Building a Machine Learning Platform

The MLOps Blog

Automation You want the ML models to keep running in a healthy state without the data scientists incurring much overhead in moving them across the different lifecycle phases. It would make sure that all development and deployment workflows use good software engineering practices. My Story DevOps Engineers Who they are?