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To bring autonomous vehicles to the consumer market, developers and data scientists need to develop, train, and deploy increasingly complex machine learning (ML) models and algorithms. Using ML and artificial intelligence solutions on AWS, they can access robust self-managed tools and scalable infrastructure to develop cost-effective models and algorithms in the cloud. With these tools in place, manufacturers can build powerful solutions to facilitate critical self-driving functions, including lane-keeping assist, adaptive cruise control, and automated emergency braking, accelerating their time to market.
Partner Solutions
Software, SaaS, or managed services from AWS Partners
More and more OEMs are in the process of bringing Level 3 and above ADAS/AD vehicles to market this also includes commercial vehicles. To optimize the time to market a solution for ADAS/AD testing is needed that offers scale i.e., running 1000s of tests simultaneously, is cloud enabled and can execute testing of ADAS/AD Features (e.g., AEB, ACC etc.), Functions (e.g. Sensor fusion etc.) and components (Camera, Lidar etc)
The KSIL platform is a cloud-based test management framework. The Platform integrates with scenario and KPI databases, enables selection of scenarios against which the feature or function should be tested, executes 1000s of test runs simultaneously and provides results against KPIs on a visualization dashboard where each scenario can be viewed (video) and also downloaded as reports. The solution has already been deployed on AWS and is being utilized for global OEMs for testing upto Level 4 autonomy
Astro is a full-lifecycle orchestration platform powered by Apache AirflowTM, the de facto standard for expressing data flows as code. It is the most efficient and effective way to run Airflow in your cloud. Built for teams of all sizes, Astro allows you to focus on your pipelines, removing undifferentiated operational burden. Build, run, and observe your data pipelines — with the core developers behind Airflow managing it all.
Weights & Biases (W&B) is the leading developer-first MLOps platform to build better models faster. Weights & Biases lets ML teams track experiments, understand model and dataset dependencies, visualize and understand their datasets, and collaborate and share findings.
Weights & Biases is trusted by more than 100K ML practitioners: Used by ML teams including at OpenAI, Toyota Research Institute, GitHub, and ML category leaders across all industries and works seamlessly with any ML framework or existing architecture