This post gives a personal insight into what Minimum Viable Product means for Machine Learning and the importance of starting small and iterating. At Pivotal Labs, I was exposed with the lean startup thinking popularized by Eric Ries.
Lean startup is basically the state-of-the art methodology for product development nowadays. Its central idea is that by building products or services iteratively by constantly integrating customer feedback you can reduce the risk that the product/service will fail (build-measure-learn).
An integral part of the build-measure-learn concept is the MVP which is essentially a “version of a new product which allows a team to collect the maximum amount of validated learning about customers with the least effort”. One well known example is to validate whether mobility will be successful or not (see image below).
We essentially start with the smallest effort to test the idea. In this case, we just take two wheels and a board.
Then we ship this to the market and get feedback to continuously improve our product by adding more complexity to it. In this case we ended up with a car that integrates the consumer’s feedback. Read more from kdnuggets.com…
thumbnail courtesy of kdnuggets.com