A Human's Guide to Machine Learning Projects
Getting a machine learning project off the ground is hard— this guide will help make it easier.
With various stakeholders, differing background knowledge among team members, and administrative hurdles, many projects die before they have a chance to fly. The solution to this problem is to build a solid project foundation from the very first stages to set yourself up for success. But how do you do that?
Martin Schmitz, our Head of Data Science Services, outlines the process that he’s successfully used with teams across different industries and use cases over the last ten years to ensure machine learning projects start off on the right foot.
In this guide, you’ll learn:
- How to respond to the most common objections about starting a machine learning project.
- The basics of CRISP-DM and why it’s the most widely used analytics process in the world.
- Why your focus should be on business outcomes, and how to keep that focus in mind throughout the project.
- What you should consider when preparing your data.
Find out how ML can transform your business
We apply our expertise to help you identify the use cases you should tackle in your organization. The outcome is an impact-feasibility map that you can use with or without us.