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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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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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Explainable AI (XAI): The Complete Guide (2024)

Viso.ai

Continuous Improvement: Data scientists face many issues after model deployment like performance degradation, data drift, etc. By understanding what goes under the hood with Explainable AI, data teams are better equipped to improve and maintain model performance, and reliability.