Markus Schmitt

About Me

Markus Schmitt is the founder and head of data science at Data Revenue, a Machine Learning Agency based in Berlin, Germany, where he builds custom end-to-end machine learning systems for Medical, Finance and Marketing clients. Before Data Revenue he developed new ventures for the company builder Team Europe and studied Mathematics & Economics at Warwick.

The 4 Success Factors of any AI Project

If you are a product manager and want to build anything with machine learning, here’s a list of the 4 most important things to keep in mind. They are prioritising engineering over data science, reducing risks by going lean, not getting distracted by the algorithm, and sharing all the business requirements with your developers. Once the engineering team starts building, they have to make a lot of choices. The better they know your priorities, the more they can make the right decisions. 

Four Most Important Success Factors in any Machine Learning Project

A machine learning project is first and foremost a software project. Many data scientists have little experience building well architected, reliable and easy to deploy software. When you build a production system, this will become a problem. As a rule of thumb, engineers can pick up data science skills faster than data scientists can pick up engineering experience. If in doubt, work with the python engineer with 5+ years experience and a passion for AI. If you are a product manager and want to build something with machine learning, here’s a list of the 4 most important things to keep in mind.

How We Find Machine Learning Use Cases

Every firm is unique. Even within narrow verticals, the overlap in what two different companies need is smaller than you might think. Don’t try to fit your business into a box. If your business has a large amount of data and you are asking yourself, “How can I use AI to build something smart from our data?” Machine learning is just a tool to automate pattern discovery and then make smart predictions based on those patterns. Most of the time, it’s about improving an existing process by making it a little bit smarter.

The Harvard Innovation Lab

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The Harvard Innovation Lab


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