Bringing an AI System from Proof of Concept to Deployment
Offered By: Toronto Machine Learning Series (TMLS) via YouTube
Course Description
Overview
Explore the journey of transforming an AI system from a proof of concept to a fully deployed solution in this insightful conference talk by James Cameron, Senior AI/ML Solutions Architect at NVIDIA. Gain valuable insights into the various stages of creating a production-grade AI system, including developing an MVP, scaling and growing systems, and performance tuning. Learn from real-world experiences as Cameron shares tips and tricks for overcoming common challenges such as sizing hardware requirements, meeting latency targets, and developing effective MLOps procedures and systems. Discover the importance of machine learning engineering in transitioning data science projects from R&D labs to practical applications, and equip yourself with the knowledge to successfully bring AI systems to life in a business environment.
Syllabus
Bringing An AI System From Proof of Concept to Deployment
Taught by
Toronto Machine Learning Series (TMLS)
Related Courses
Machine Learning Operations (MLOps): Getting StartedGoogle Cloud via Coursera Проектирование и реализация систем машинного обучения
Higher School of Economics via Coursera Demystifying Machine Learning Operations (MLOps)
Pluralsight Machine Learning Engineer with Microsoft Azure
Microsoft via Udacity Machine Learning Engineering for Production (MLOps)
DeepLearning.AI via Coursera