Increasing the likelihood of success
Every machine learning effort requires a programmatic approach. It starts with a feasibility study: Is it even possible to solve a given data problem with the available data and the requirements of the business? Through careful exploratory machine learning work, we validate the feasibility of machine learning project ambition. Sometimes success can even be measured in cost savings from identifying an effort that won’t pan out.
Rules of engagement
Our three-phase process aims to usher your project from science to engineering, starting with proof of concept, carefully documenting what worked and didn't work, and ending with the handoff from your data scientists to production. The length of a typical engagement depends on the complexity of the project.
We break it up into three phases:
- Exploration (two weeks)
- Algorithmic excellence (a few weeks to a couple of months)
- Operationalization (a few weeks to a couple of months)