The Machine Learning Problem You Don’t Know You Have . . . Yet
The Machine Learning Problem You Don’t Know You Have . . . Yet


So you’re doing some machine learning. But have you really thought about what needs to happen once you put it into production? This is the challenge lurking behind every promising machine learning initiative: making it work in the real world.
Robbie Allen, author of the book Machine Learning in Practice and the CEO of two data-centered companies, breaks the challenge down into key production issues like:
On-Prem Deployment. If you’re not in the cloud, deployment on your own servers or hardware can be difficult.
Workflow Integration. When companies work with legacy and/or closed systems, connecting prediction API’s into products/workflows is hard.
Who Owns Deployment and Maintenance? Is DevOps responsible for maintenance and deployment of ML models into production? Is this a job for data scientists? Engineers? When data scientists build models, will they see the light of day?
Monitoring. Today’s tools lack the ability to measure model drift and identify when a model’s production data is no longer representative of training data.
Still Learning? Machine learning is not “learning” unless there is a continuous feedback loop of data from production; many ML solutions do not have an established method for doing this.
Changing It Up. Measuring and adjusting machine learning models is challenging enough; these tasks are even harder when dealing with changing models, parameters, data labels, and goals.


Robbie Allen is the CEO of Infinia ML, a team of advanced machine learning experts focused on making business impact. The company helps Fortune 500 companies and cutting-edge startups reduce costs, increase efficiency, and achieve breakthroughs with data science. Infinia ML serves industries from manufacturing and healthcare to marketing and human resources. The company’s capabilities include natural language processing, recommendation engines, object detection, 3D image modeling, and anomaly detection.
Previously, Robbie founded and led Automated Insights, whose natural language generation software helps automate content production for The Associated Press, Yahoo!, and many others. Automated Insights was successfully acquired by Vista Equity Partners in 2015, and Robbie currently serves as the company’s Executive Chairman. Before starting Automated Insights, Robbie was a Distinguished Engineer at Cisco. Robbie has authored or coauthored eight software books, owns six patents, and has spoken at a variety of conferences including the O’Reilly AI Conference, Strata, SXSW, and the MIT Sloan CIO Symposium. He holds two Master’s degrees from MIT and is completing his Ph.D. in computer science at UNC-Chapel Hill.

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