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Lesson 4: Openness and Interoperability of Machine Learning Environments

Lesson 5: Training ML Models at Scale

Lesson 6: Securing your Machine Learning Environments

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Featured Blogger of the Month & More Content

Featured Speakers of the Month:

Minsoo Thigpen

About the author:

Minsoo is a Product Manager at Microsoft Azure Machine Learning designing and building out Responsible AI tools for data scientists. She has bachelor’s degrees in Applied Mathematics and Painting from Brown University and Rhode Island School of Design (RISD). Coming from an interdisciplinary background with experience in building machine learning models and products, analyzing data, and designing UX, she is always finding work at the intersection of AI/ML, design, and social sciences to empower data and ML practitioners to work ethically and responsibly end-to-end.

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Azure Machine Learning

The Azure Machine Learning service empowers data scientists with a wide range of productive experiences for building, training, and deploying machine learning models faster. Accelerate time to market and foster team collaboration with industry-leading MLOps—DevOps for machine learning. Innovate on a secure, trusted platform, designed for responsible machine learning.

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