AI & Data · Autonomous vehicles
99.9% accurate training data for safer autonomous driving
A pioneer in the autonomous vehicle industry
The challenge
What the client was trying to solve
Self-driving algorithms needed a continuous, accurate pipeline of training data and model development that could scale with demand.
The business problem
- 01
Scaling training data production and ML operations for continuous model development and retraining.
- 02
The need for a reliable partner to run the data pipeline continuously.
- 03
Expertise required across advanced tools, and a workforce flexible enough to match dynamic demand.
Branta's approach
How we worked back from the outcome
A 200+ person core-flex team running the training-data and model-development pipeline with quality management built in.
Deployed a team of more than 200 people in a core-flex model for agility and scalability.
Trained the team continuously on the tools involved: AWS, the proprietary VSERVE platform, SamaSource and Salesforce integrations.
Ran meticulous processes with a dedicated focus on quality management.
Architecture
Problem, thinking, technology, outcome
The system as it runs, from the business problem on the left to the measured result on the right.
- Business problem
Raw driving data
Continuous input for self-driving algorithms
- Branta thinking
Core-flex team
200+ people scaling with demand
- Technology
AWS and VSERVE
Data pipeline and proprietary tooling
- Technology
SamaSource and Salesforce
Annotation and integration tooling
- Measured outcome
Model training and retraining
99.9% delivery accuracy
Technology
- AWS
- VSERVE
- SamaSource
- Salesforce
The outcome
Measured, not described
- 99.9%
- delivery accuracy
- 200+
- person core-flex team
- Better product safety and performance through higher-quality self-driving algorithms.
- Seamless scalability and operational excellence.
What changed
The client gained a training-data operation that scales with demand and delivers at 99.9% accuracy, which is what safer self-driving algorithms depend on.
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