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MLOps and the evolution of data science

IBM Journey to AI blog

Because ML systems require significant resources and hands-on time from often disparate teams, problems arose from lack of collaboration and simple misunderstandings between data scientists and IT teams about how to build out the best process. How to use ML to automate the refining process into a cyclical ML process.

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Promote pipelines in a multi-environment setup using Amazon SageMaker Model Registry, HashiCorp Terraform, GitHub, and Jenkins CI/CD

AWS Machine Learning Blog

Machine Learning Engineer with AWS Professional Services. She is passionate about developing, deploying, and explaining AI/ ML solutions across various domains. Prior to this role, she led multiple initiatives as a data scientist and ML engineer with top global firms in the financial and retail space.

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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.