ML Platform Beyond Kaggle Paradigm - Policy-Centric Approach for End-to-End Automation
Offered By: MLOps World: Machine Learning in Production via YouTube
Course Description
Overview
Explore a groundbreaking approach to ML platforms that goes beyond the traditional "Kaggle Paradigm" in this 24-minute conference talk by Norm Zhou, Founding Engineer at a Stealth Startup. Discover how a policy-centric view can revolutionize ML system building and increase engineering productivity. Learn about two major additions to the model-centric approach: a fully managed unified data collection system and downstream extension to A/B testing systems. Understand how these innovations enable end-to-end automation, directly improving business metrics and addressing changing business needs more effectively. Gain insights into the limitations of current ML system building approaches and explore a new paradigm that promises to enhance intelligent data-driven applications with limited engineering effort.
Syllabus
ML Platform Beyond Kaggle Paradigm
Taught by
MLOps World: Machine Learning in Production
Related Courses
Developing a Tabular Data ModelMicrosoft via edX Data Science in Action - Building a Predictive Churn Model
SAP Learning Serverless Machine Learning with Tensorflow on Google Cloud Platform 日本語版
Google Cloud via Coursera Intro to TensorFlow em Português Brasileiro
Google Cloud via Coursera Serverless Machine Learning con TensorFlow en GCP
Google Cloud via Coursera