August 2020 Summaries

TiE50 Awards recognize innovative startups like Arize AI, a pioneer in machine learning observability platforms, which helps businesses troubleshoot and explain their models' performance, making it crucial for successful model deployment. The awards program is ten years old and has attracted high-potential startups from different parts of the world, providing recognition and opportunities to pitch to investors and entrepreneurs. Arize AI's platform provides a real-time solution to monitor, explain, and troubleshoot issues as models move from research to production, showcasing its innovative approach in the ML observability space.

Manisha Sharma has joined Arize AI's Frontend Engineering team. Previously, she worked as a Frontend Engineer at Slack and on data visualization and design systems at Pandora Music. She holds a bachelor’s degree in Cognitive Science from UC Berkeley. Manisha is passionate about inclusiveness, accessibility, and transparency in the tech field. She has taught programming classes with organizations like Black Girls Code, Girls Who Code, and Code Nation. Manisha envisions a future with responsible and ethical AI and believes that it's necessary to build tools that provide insight and explainability to critical decisions made by AI.

The machine learning infrastructure space is complex and crowded, with various platforms offering different functions across the model building workflow. Understanding the goals and challenges of each stage of the workflow can help businesses make informed decisions on which ML infrastructure platforms to use. The production environment is a critical part of the model lifecycle, where the model touches the business and makes decisions that improve outcomes or cause issues for customers. However, transitioning from a research environment to a production engineering environment poses unique challenges, such as moving from rapid experimentation in Jupyter Notebooks to software engineering environments with version control, test coverage analysis, and reproducibility. Model validation is critical to delivering models that work in production, involving testing model assumptions, demonstrating how well a model will work under different environments, and ensuring the model's performance matches expectations. ML infrastructure tools can help with model validation by providing repeatable and reproducible tests, enabling organizations to reduce time to operationalize models and deliver models with confidence.