B to B Machine Learning

Type of companies and their goals and implications.

Defensible Machine Learning    via O'Reilly  in Basecase.vc

By Ankur Goyal, Alana Anderson  March 3, 2021

B2B machine learning (ML) companies are an enigma: they have the opportunity to revolutionize how we do business, but they look & feel quite different from their traditional SaaS counterparts and have proven difficult to scale. In this post, we aim to demystify the challenges of building an enduring ML company by providing a simple framework for the three ways to do so. For each, we outline common attributes of successful companies and walk through potential pitfalls. These learnings are based on our experience building Impira and evaluating companies at Base Case Capital.

For aspiring entrepreneurs thinking about problems in this area, we hope this gives you a starting point to understand the landscape, frame your idea, and avoid the challenges that may arise.

Three types of ML companies:

Enterprises who seek to solve problems with machine learning have two options: (1) develop ML models in-house or (2) purchase software that has embedded ML. Startups can play a significant role in both scenarios. The first option involves providing in-house users, traditionally referred to as ML practitioners, with ML systems that enable them to work better, faster, and smarter to develop and operate models. The second option is usually packaged as a Powered-by-ML solution, which solves a specific business challenge with the help of machine learning.

There is an emerging third group, AutoML solutions, which allow non-ML practitioners to develop their own models. This is somewhat a hybrid of the first two, and correspondingly represents a massive opportunity, but with significant product and technical risks. In fact, many of the common pitfalls for both ML systems and Powered-by-ML solutions can be overcome with AutoML. We’ll discuss this in depth below. .."