Autonomous vehicles

Autonomous driving is a pipedream without automated, reproducible and explainable machine learning pipelines

Autonomous vehicles are set to change the future of global transportation: reducing congestion, road accidents and carbon dioxide emissions with some estimates predicting the autonomous vehicle market growing almost tenfold between 2019 and 2026.

In order to succeed with autonomous vehicles AI teams need to overcome both technical and societal challenges, and the two are intertwined. To convince the public and regulators that autonomous vehicles are safe it is important to be able to reproduce failures and redeploy updated models quickly. The same need for reproducibility impacts time-to-market, with machine learning engineers needing to reproduce the work of others in order to collaborate efficiently when training computer vision models on petabyte scale training sets.

How can Dotscience help?

Dotscience gives your AI teams the tools and processes they need to handle massive data and end to end model lifecycle management, while providing both insight for managers & customers, and oversight for auditors & regulators.

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Key AI challenges

  • Quickly understanding & reproducing failures in the field
  • Enabling rapid collaboration between machine learning engineers when working with petascale datasets
  • Managing model life cycles across teams and heterogeneous hardware
  • Complying with regulatory frameworks when developing machine learning models

Dotscience gives your AI teams the tools and processes they need to handle massive data and end to end model lifecycle management, while providing both insight for managers & customers, and oversight for auditors & regulators.

Learn more