Introduction to Machine Learning in Production
Learn the components of an ML production system and develop end-to-end skills in scoping, data needs, modeling, and deployment. Build a strong AI career with machine learning engineering for production, combining ML concepts with software development expertise. Explore the ML lifecycle, model selection and training, and data definition in just 3 weeks.
In the first course of Machine Learning Engineering for Production Specialization, you will identify the various components and design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment constraints and requirements; and learn how to establish a model baseline, address concept drift, and prototype the process for developing, deploying, and continuously improving a productionized ML application.
Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills.
Week 1: Overview of the ML Lifecycle and Deployment
Week 2: Selecting and Training a Model
Week 3: Data Definition and Baseline
User Reviews
Be the first to review “Introduction to Machine Learning in Production”
You must be logged in to post a review.
There are no reviews yet.