- aws
- ml
- ai
- cloud
- autonomous-driving
- llm
- agentic-ai
- rag
- multi-tenant
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AWS Guidance for AI-Driven Robotic Simulation and Training on AWS
Build an AI-powered robot training and fleet management system using Amazon Bedrock foundation models and AWS IoT. Combines imitation learning with NVIDIA Isaac on Amazon EC2 and reinforcement learning with edge-optimized reward functions to train robots for precise tasks and manage fleets at scale.
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How BMW Group and Qualcomm built an automated driving platform on AWS
End-to-end automated driving platform combining Qualcomm's in-vehicle compute with AWS cloud services. Enables scalable data processing, large-scale simulation, and continuous L2+ feature development — from data collection in the vehicle to model training and validation in the cloud.
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Scaling LLM Inference on EKS with AWS Inferentia and Trainium
Deploy and scale large language model inference workloads on Amazon EKS using AWS Inferentia and Trainium accelerators. Covers model compilation with Neuron SDK, container packaging, and Kubernetes-native autoscaling to achieve cost-efficient, low-latency serving at production scale.
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Secure and Flexible Access Control for Regulated Workloads: A Guide to Implementing Role-Based Access
Implement fine-grained role-based access control for regulated industries running workloads on AWS. Walks through IAM policy design, permission boundaries, and service control policies that balance security compliance with developer agility across multi-account environments.
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Mastering the Future of Autonomous Driving with End-to-End AI
Explore how end-to-end AI models are reshaping autonomous driving by replacing modular perception-planning-control pipelines with unified neural networks. Discusses vision-language models, sensor fusion strategies, and the infrastructure required to train and validate these systems at scale on AWS.
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How to Deploy Trained Models to Robots After Using Functions Generated by Large Models
Bridge the gap between foundation model outputs and real-world robotic deployment. Covers the workflow of using large language models to generate task functions, training robot policies via simulation, and deploying optimized models to edge devices for reliable physical execution.
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How to Fine-Tune a 6B LLM Simply and Cost-Effective Using Ray AI Runtime on Amazon EKS
Fine-tune a 6-billion parameter language model using Ray AI Runtime on Amazon EKS without managing complex distributed training infrastructure. Demonstrates parameter-efficient tuning with LoRA, Ray Train for distributed scaling, and cost optimization strategies using spot instances and right-sized GPU nodes.
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How to Build a Successful Open Source Project for Autonomous Driving
Lessons learned from building and growing open source projects in the autonomous driving space. Covers community engagement strategies, contribution guidelines, modular architecture design, and how to balance enterprise requirements with open collaboration to accelerate industry-wide innovation.