A Human’s Guide to Machine Learning Projects

Humans-Guide-ML-Projects

Getting a machine learning project off the ground is hard—the process outlined in this guide will help make it easier. 

Click the button and chat with Marla to access the whitepaper. Scroll down to learn more. 

Getting a machine learning project off the ground is hard—the process outlined in this guide will help make it easier. 

Click the button to access the whitepaper.
Scroll down to learn more. 
Humans-Guide-ML-Projects

Why you should read this whitepaper

Getting a machine learning project off the ground is hard. 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 project 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. By setting appropriate expectations, getting buy-in early on, and making sure that your project is tied to a clear business problem or need, you’ll be better placed to create long-term value and thus long-term success. 

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. 
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