It's an arms race to achieve full self driving capability, and the competition is immense. Within a few short years, the market will expect self-driving to come as standard, and the only way to achieve it is to invest in and succeed in building cutting-edge AI teams. It's a tall order.
There couldn't be a better example of an application of AI that simultaneously demands rapid iteration of model development, while requiring a strong audit trail and forensic debugging capabilities. If an autonomous vehicle is involved in an accident, the ability to track the provenance of the model down to the exact raw data files that were used as input for training is clearly essential.
Dotscience delivers "your own Michaelangelo" out of the box on day one. It enables internal teams to seamlessly build, deploy, and operate machine learning solutions at massive scale, and is designed to cover the end-to-end ML workflow: manage data, train, evaluate, and deploy models, make predictions, and monitor predictions.
Stop building bespoke one-off systems to use your models in production — Dotscience provides reliable, uniform, and reproducible pipelines for creating and managing training and prediction data at scale. Crucially, it provides a standard place to store the results of training experiments and an easy way to compare one experiment to another. And it makes deploying a model into production and statistically monitoring it as easy as clicking a button or making an API call.