Autonomous Vehicles.

The first company to achieve full self driving capability will be richly rewarded.

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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.

Key AI challenges

  • Companies like Uber have already developed their own model management solutions like Michelangelo. To be competitive, you need your own Michelangelo, but you don't have the time or the skill set to build an AI platform from scratch.
  • Training autonomous vehicle models uses enormous quantities of data. You need a platform that can connect to external sources of data, such as in S3, and yet still track provenance for forensic debugging capabilities — in the case of a crash, you need to know what went wrong.
  • Legacy approaches to model and data management like using a big shared filesystem and manually rsync'ing files around when you want to make a copy just won't cut it. There's too much room for human error and no audit trail if something goes wrong.
How can Dotscience help?

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.