At betaworks, we are in an unique position to be able to work with different kinds of data due to the diversity of our portfolio companies, including weather, audio, video, natural language text, gifs, images, gaming data etc. Our Machine Learning solutions have to adapt to time, human and product technology constraints at startup pace. Many times, these solutions become the integral factor in scaling a company. Therefore, ML efforts must oscillate between fast and slow phases of analysis, modeling, planning, building, deployment, evaluation and tuning - so we can streamline ML with other parts of the organization and build synergy.

It's a complex challenge to adjust machine learning to product, customer, team and technological constraints in the real world. These are things they don’t really teach you when you learn the math behind machine learning. Folks from different backgrounds - data scientists, machine learning engineers, statisticians, backend engineers work together to build a ML solution and many times, the synergy is actually hindered by the different sort of constraints and priorities these people have. Startups evolve very fast. In the process of moving fast, you don’t want to just deploy a model without sufficient forethought - because your initial product features can be deal breakers.

People who come from different backgrounds into machine learning have different ideas about how to achieve this in the real world. A backend engineer versus the data scientist who comes from computer science vs. the statistician who comes from economics - each has very different ideas about how this solution process should work. At betaworks, data scientists work closely at the product level - with designers, developers, engineers and hackers, which isn’t an unfamiliar scenario to many startups. Sometimes, a bigger challenge than the accuracy of your model is synergizing all the help that is tasked to consolidate a model into the product.

I think if you are making a simple app, then sometimes it isn’t very important initially. But a lot of times, simplicity tends to hide the actual complexity behind a product. I will give you an example. We have a company called Poncho. Poncho is this cat who sends you texts messages every morning and evening about the weather, but in like a really funny and friendly way. Editors write the messages for the consumers. But behind Poncho’s friendly message interface, there is massive clustering of the weather patterns across all US zip codes. So that requires a lot of machine learning. And when Poncho started to scale from the New York area to other parts of the country (its national now), it was challenging to actually process that much information in real time. We had to calculate these clusters of geo locations that have similar patterns using weather time series data and then ask editors to write one message per cluster. So that is an example when machine learning is absolutely critical in scaling a startup. Depending on the company, sometimes the core technology is strongly machine learning heavy. And yet, it might be behind a veil of simplicity.

If you are thinking of deploying or already having ML systems running in your company, what are the key facts you should know when building and interacting with such systems or with people that run them? What are the capabilities, limits and evolution patterns of such systems? When should you move fast vs. move cautiously around a ML solution?