Designing Machine Learning Systems in the Cloud

Designing Machine Learning Systems in the Cloud

This course is an introduction to designing and implementing production machine learning (ML) systems that will give students the skills necessary to architect and deploy ML solutions to the cloud. This course is for anyone with at least 2 years of programming experience and familiarity with ML looking to deliver real business value with ML in real, enterprise software systems.Using Python, Docker, Kubernetes, and Google Cloud, students will learn the practical skills necessary to build end-to-end ML systems, from engineering training data, features and data pipelines to deploying and monitoring ML models for real-time inference in production.What you’ll learn and how you can apply itExplain, design and implement components of a production ML systemThis course is for you because…You’re an ML practitioner interested in learning to take your models from development to production, enterprise systems.You’re a software engineer looking for the specific experience required to design and build ML-powered systems.You’re an ML enthusiastic looking for the theoretical and practical skills required to train, evaluate, deploy and monitors models in real-world settings.Prerequisites:2 years of object-oriented programming experience (preferably Python)1 year of experience working with a cloud provider (Google Cloud, Amazon Web Services, etc..)Beginner knowledge of systems design conceptsBeginner knowledge of data engineering conceptsBeginner knowledge of experience using linuxLabels: Kyle Gallatin,data,video,machine Learning,