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This Guidance illustrates how to deploy and use the AWS DeepRacer Chatbot, an intelligent virtual assistant powered by multimodal generative AI and domain adaptation techniques. The chatbot combines large language models, conversational log analysis, question and answer support, and code generation capabilities to deliver a comprehensive and intelligent approach that is tailored for AWS DeepRacer. Featuring a user-friendly interface and advanced AI capabilities, the AWS DeepRacer Chatbot generates custom Python reward functions aligned with autonomous racing user requirements, streamlining model workflows, and assisting users towards improved model performance.
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Architecture Diagram
![](https://d1.awsstatic.com/apac/events/2021/aws-innovate-aiml/2022/eng/innovate-aiml-22-UI_Gradient-Divider.082bb46e8d9654e48f62bf018e131dd8ec563c4e.jpg)
[Architecture diagram description]
Step 1
The users navigate to the Amazon CloudFront URL to fetch the AWS DeepRacer Chatbot webapp.
Get Started
![](https://d1.awsstatic.com/apac/events/2021/aws-innovate-aiml/2022/eng/innovate-aiml-22-UI_Gradient-Divider.082bb46e8d9654e48f62bf018e131dd8ec563c4e.jpg)
Deploy this Guidance
Well-Architected Pillars
![](https://d1.awsstatic.com/apac/events/2021/aws-innovate-aiml/2022/eng/innovate-aiml-22-UI_Gradient-Divider.082bb46e8d9654e48f62bf018e131dd8ec563c4e.jpg)
The AWS Well-Architected Framework helps you understand the pros and cons of the decisions you make when building systems in the cloud. The six pillars of the Framework allow you to learn architectural best practices for designing and operating reliable, secure, efficient, cost-effective, and sustainable systems. Using the AWS Well-Architected Tool, available at no charge in the AWS Management Console, you can review your workloads against these best practices by answering a set of questions for each pillar.
The architecture diagram above is an example of a Solution created with Well-Architected best practices in mind. To be fully Well-Architected, you should follow as many Well-Architected best practices as possible.
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Operational Excellence
Amazon CloudWatch, AWS X-Ray, and AWS AppSync are a suite of services that work in tandem to assist users in effectively running and monitoring their systems. CloudWatch enables administrators to monitor their chatbot applications, collecting logs and metrics to gain valuable insights into performance and usage patterns. X-Ray provides distributed tracing capabilities, empowering developers to visualize and analyze the flow of requests through their serverless architecture. AWS AppSync offers real-time data synchronization and offline capabilities, enhancing the chatbot's responsiveness and reliability. Collectively, these services equip users with the tools to instrument their chatbots effectively, understand their state, and continuously improve their operations based on real-time data and insights.
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Security
The AWS DeepRacer Chatbot uses a variety of AWS security services, including Amazon Cognito, AWS Identity and Access Management (IAM), and AWS AppSync, to protect both information and systems. For example, Amazon Cognito provides user authentication and authorization for the chatbot, securing access to the AWS AppSync APIs. IAM policies are configured with the principle of least privilege so that Lambda functions and other resources possess only the necessary permissions to carry out their respective operations. Furthermore, the built-in security features AWS AppSync, combined with the integration of Amazon Cognito, safeguard the GraphQL API endpoints.
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Reliability
The AWS DeepRacer Chatbot centers around a highly available and fault-tolerant approach comprising Lambda, DynamoDB, and AWS AppSync. Lambda and its serverless architecture facilitate automatic scaling and high availability for the chatbot to effectively handle fluctuating workloads. DynamoDB, a highly available and durable database, serves as the repository for storing conversation history and other persistent data. AWS AppSync, on the other hand, offers real-time data synchronization and offline support, enhancing the chatbot's ability to maintain state and recover from network interruptions. Furthermore, the use of an AWS Cloud Development Kit (AWS CDK) for infrastructure deployment enables consistent and repeatable setups, thereby reducing the risk of configuration errors and improving the overall reliability of the system.
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Performance Efficiency
The AWS DeepRacer Chatbot harnesses the scalable and performant services of the AWS Cloud, including the LLMs provided by Amazon Bedrock, to deliver fast and responsive interactions. This enables AWS customers to maximize the performance of their AWS DeepRacer models. Amazon Bedrock offers foundation models that can be seamlessly integrated and scaled without the need for extensive infrastructure management.
Moreover, the serverless compute capabilities of Lambda allow for efficient resource utilization, automatically scaling based on demand. DynamoDB, with its single-digit millisecond latency for data retrieval, allows for fast access to conversation history and other pertinent data. The synergistic integration of these services allows users to build high-performance chatbots capable of handling complex queries and real-time interactions efficiently.
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Cost Optimization
The serverless architecture using Lambda, combined with the pay-per-use pricing models of Amazon Bedrock and DynamoDB, helps optimize costs in this Guidance. Lambda functions only incur charges when invoked, minimizing idle resource expenses. The pricing model of Amazon Bedrock enables participants to experiment with advanced AI capabilities without upfront investments in model training or infrastructure. Finally, the on-demand capacity mode of DynamoDB helps ensure participants only pay for the actual read and write requests made by their chatbots.
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Sustainability
The serverless services selected for this Guidance automatically scale resources based on actual usage, minimizing idle capacity and reducing energy consumption. In addition, the use of pre-trained models in Amazon Bedrock eliminates the need for energy-intensive model training on dedicated hardware. Traditional machine learning model training often requires significant computational resources and energy consumption, as the models need to be trained from the start on large datasets. However, by using pre-trained models available in Amazon Bedrock, the AWS DeepRacer Chatbot can bypass this energy-intensive training process and instead fine-tune the existing models specifically for AWS DeepRacer. This approach not only saves on energy usage but also reduces the carbon footprint associated with the chatbot's development and deployment.
Related Content
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Disclaimer
The sample code; software libraries; command line tools; proofs of concept; templates; or other related technology (including any of the foregoing that are provided by our personnel) is provided to you as AWS Content under the AWS Customer Agreement, or the relevant written agreement between you and AWS (whichever applies). You should not use this AWS Content in your production accounts, or on production or other critical data. You are responsible for testing, securing, and optimizing the AWS Content, such as sample code, as appropriate for production grade use based on your specific quality control practices and standards. Deploying AWS Content may incur AWS charges for creating or using AWS chargeable resources, such as running Amazon EC2 instances or using Amazon S3 storage.
References to third-party services or organizations in this Guidance do not imply an endorsement, sponsorship, or affiliation between Amazon or AWS and the third party. Guidance from AWS is a technical starting point, and you can customize your integration with third-party services when you deploy the architecture.