Chris Gregory
About Chris Gregory
Chris Gregory is an ML Engineering Manager at Microsoft, known for his contributions to Azure Machine Learning, AutoML, and Microsoft's Responsible AI Toolbox.
ML Engineering Manager at Microsoft
Chris Gregory serves as an ML Engineering Manager at Microsoft. In this role, he has been instrumental in developing various services and open source toolkits for the Azure Machine Learning platform. His work primarily focuses on enhancing the machine learning ecosystem within Microsoft, ensuring that the tools and services meet high standards of functionality and usability.
Azure Machine Learning Platform Services
Chris Gregory has been actively involved in developing services and open source toolkits for the Azure Machine Learning platform. His contributions have bolstered the platform's capabilities, making it easier for users to implement machine learning solutions. The developed toolkits help in simplifying tasks like data preprocessing, model training, and deployment.
Model Selection Recommender System for AutoML
On the AutoML team, Chris Gregory played a key role in building out the training infrastructure for the model selection recommender system. His efforts have contributed to streamlining the process of selecting the most appropriate machine learning models based on various criteria, improving the overall efficiency and performance of the AutoML system.
Model Evaluation Suite for Classification and Forecasting Models
Chris Gregory led the development of a comprehensive model evaluation suite for both classification and forecasting models. This suite aids in the thorough assessment of machine learning models, ensuring they meet required performance standards. The evaluation tools he developed are critical for validating the accuracy and reliability of different machine learning models used within Microsoft.
Microsoft's Responsible AI Toolbox
As part of Microsoft's commitment to responsible AI, Chris Gregory has contributed to interpretability methods, including feature importance and causal analysis. His work was integral to the launch of the Responsible AI Toolbox, which provides a suite of tools designed to help practitioners understand, diagnose, and mitigate issues in machine learning models. These tools are essential for ensuring the transparency and fairness of AI systems.