This course will help you and your team understand the concepts and best practices needed to scale up your Machine Learning Operations (MLOps) in your machine learning projects. It provides ML project team members with an extensive overview of the challenges and decisions encountered in building professional ML systems. Rather than focusing on evolving tools, the course emphasizes concepts and frameworks that help to share a common understanding of MLOps within ML teams. The course is structured around the ML Lifecycle, a key perspective on MLOps, from planning a machine learning project to implementing feedback loops after your project is deployed. Among other things, you will learn about how to plan an ML project, how to apply the appliedAI Project Management Framework to your ML projects, how various accountabilities should be involved during the different project phases and how the ML Principles function as the foundation of MLOps. The course comes with a video series and a workbook that you can keep to easily access the course content. You work with your own copy of the workbook in the form of a PDF file, digital whiteboard, or physical copy and work alongside the video series.
From open data to local impact: An MCP server and...
Imagine you are a climate researcher studying drought patterns across East Africa. The satellite data exists, hosted free on AWS,...
AI for Good | Global Summit
Building the infrastructure for more equitable access to AI
As AI moves into everyday services, the infrastructure beneath it is becoming increasingly important. Connectivity, computing capacity, energy efficiency, security,...
AI for Good | Global Summit
Japan Pavilion showcases AI startups tackling global challenges
During the session Innovative Japanese AI start-ups addressing social challenges,hosted by the Japan Pavilion at the AI for Good Summit...
AI for Good | Global Summit
