Machine learning infrastructure includes the resources, processes, and tooling needed to develop, train, and operate machine learning models. The purpose is to develop tooling for data scientists and machine learning engineers to use for getting their machine learning model prototypes to production. This presentation will touch on various aspects of the infrastructure from data collection, feature extraction, training and retraining, serving, and monitoring.
Join this session, if you:
Would like to learn about the infrastructure needed for machine learning
Are looking to introduce machine learning models in your company
Are interested in advanced in business innovations
Please note: The views expressed by speakers or moderators are those of the speaker or moderators and not, necessarily, of Oxylabs or other respective organizations. Before engaging in scraping activities of any kind, you should consult your legal advisors.
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Meet the speaker
Pujaa is a machine learning engineer at Stripe. She's been recognized for her work as a Google Machine Learning Developer Expert and Google Women Techmaker. Previously, she founded the USA chapter of WomenInAI, worked at an AI startup that was acquired, and moved to Silicon Valley from Wall Street. She also advises start ups, venture capital firms, and policymakers on AI.
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