AWS DeepRacer vs OpenVINO: What are the differences?

Introduction

Here are the key differences between AWS DeepRacer and OpenVINO.

1. Machine Learning Frameworks: AWS DeepRacer is built on the reinforcement learning framework, allowing users to train models through self-play and racing simulations. OpenVINO, on the other hand, is an open-source toolkit designed for optimizing and deploying deep learning models on various Intel hardware platforms.

2. Training Environment: AWS DeepRacer provides a cloud-based training environment where users can easily access resources and scale up their training workloads. In contrast, OpenVINO requires users to set up their own local environment for model training and deployment.

3. Hardware Support: AWS DeepRacer is specifically tailored for the DeepRacer car, which comes equipped with the necessary sensors, actuators, and compute power for training and racing. OpenVINO, on the other hand, supports a wide range of Intel-based hardware platforms, allowing for more flexibility in deployment options.

4. Community and Resources: AWS DeepRacer has a dedicated community of developers and enthusiasts who actively participate in races, challenges, and forums, providing a rich source of learning and collaboration. OpenVINO also has a supportive community but may not be as specialized towards autonomous racing applications.

5. Ease of Deployment: AWS DeepRacer offers a seamless deployment process on the AWS cloud infrastructure, providing easy scaling and management capabilities. OpenVINO, while powerful, may require more manual configuration and setup for deployment on different hardware architectures.

6. Cost Considerations: AWS DeepRacer involves costs for cloud computing resources, training simulations, and physical DeepRacer car purchases. OpenVINO, being an open-source toolkit, is more cost-effective in terms of software licensing but may incur costs related to hardware setup and maintenance.

In Summary, AWS DeepRacer and OpenVINO differ in terms of machine learning frameworks, training environments, hardware support, community resources, ease of deployment, and cost considerations.