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This Guidance shows how you can enhance your restaurant’s operational efficiency and employee experience through Internet of Things (IoT)–connected kitchen devices. By integrating AWS services with smart kitchen equipment (such as thermostats, robots, dishwashers, refrigerators, ovens, and grills), you can enable predictive maintenance powered by machine learning (ML), near real-time equipment, and inventory monitoring, while also gaining visibility into functional robotic equipment. Through this Guidance, you can generate data-driven insights that minimize equipment downtime, streamline kitchen operations, and enable informed decision-making, ultimately helping you reduce waste and increase revenue.
Please note: [Disclaimer]
Architecture Diagram
[Architecture diagram description]
Step 1
Use an AWS IoT Greengrass core device to connect, publish, and subscribe to data from IoT sensors on kitchen equipment on the edge over the open standard Message Queuing Telemetry Transport (MQTT) protocol.
Step 2
Use AWS IoT Core to maintain shadows of all IoT devices, connect to AWS, and manage messages from IoT sensors for further processing.
Step 3
Create a detector model in AWS IoT Events with AWS IoT Core as the input source. Configure Amazon Simple Notification Service (Amazon SNS) in the detector model to send notifications by SMS or email when an unusual event occurs or when a sensor reaches your set thresholds.
Step 4
Use AWS IoT Analytics to aggregate, transform, and analyze IoT messages from AWS IoT Core. Build an IoT analysis dashboard and visualizations on Amazon QuickSight.
Step 5
Configure an IoT rule to send messages from AWS IoT Core to Amazon Kinesis Data Streams for downstream processing.
Step 6
Use an AWS Lambda function to process messages from Kinesis Data Streams, and store them in Amazon DynamoDB.
Step 7
Amazon Kinesis Data Firehose reads data from Kinesis Data Streams and stores it in a data lake built on Amazon Simple Storage Service (Amazon S3).
Step 8
Use Amazon SageMaker to build, train, and validate ML models for predictive maintenance and anomaly detection for your kitchen equipment. Optionally, use this ML model inference with an AWS IoT Greengrass core device on the edge.
Step 9
Use a Lambda function to process all IoT data stored on a DynamoDB table and fetch the ML model inference endpoint for predictions. Create a REST API with a Lambda function as a backend on Amazon API Gateway.
Step 10
Create a kitchen operations web application that centralizes equipment monitoring and predictive maintenance capabilities. Integrate a QuickSight dashboard using QuickSight embeddings.
Step 1
Use an AWS IoT Greengrass core device to connect, publish, and subscribe to data from IoT sensors on kitchen equipment on the edge over the open standard Message Queuing Telemetry Transport (MQTT) protocol.
Step 2
Use AWS IoT Core to maintain shadows of all IoT devices, connect to AWS, and manage messages from IoT sensors for further processing.
Step 3
Create a detector model in AWS IoT Events with AWS IoT Core as the input source. Configure Amazon Simple Notification Service (Amazon SNS) in the detector model to send notifications by SMS or email when an unusual event occurs or when a sensor reaches your set thresholds.
Step 4
Use AWS IoT Analytics to aggregate, transform, and analyze IoT messages from AWS IoT Core. Build an IoT analysis dashboard and visualizations on Amazon QuickSight.
Step 5
Configure an IoT rule to send messages from AWS IoT Core to Amazon Kinesis Data Streams for downstream processing.
Step 6
Use an AWS Lambda function to process messages from Kinesis Data Streams, and store them in Amazon DynamoDB.
Step 7
Amazon Kinesis Data Firehose reads data from Kinesis Data Streams and stores it in a data lake built on Amazon Simple Storage Service (Amazon S3).
Step 8
Use Amazon SageMaker to build, train, and validate ML models for predictive maintenance and anomaly detection for your kitchen equipment. Optionally, use this ML model inference with an AWS IoT Greengrass core device on the edge.
Step 9
Use a Lambda function to process all IoT data stored on a DynamoDB table and fetch the ML model inference endpoint for predictions. Create a REST API with a Lambda function as a backend on Amazon API Gateway.
Step 10
Create a kitchen operations web application that centralizes equipment monitoring and predictive maintenance capabilities. Integrate a QuickSight dashboard using QuickSight embeddings.
Well-Architected Pillars
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 ExcellenceRead the Operational Excellence whitepaper
Amazon CloudWatch provides visibility into infrastructure and application performance. This enables proactive monitoring, fast troubleshooting of errors and issues, and near real-time responses to events and incidents.
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SecurityRead the Security whitepaper
API Gateway enhances security for backend services and data by providing authentication and access control through API keys, helping you restrict access to your APIs and limit API rates using throttling. When used with API Gateway, AWS Identity and Access Management (IAM) provides fine-grained access controls to your APIs and SSL policies. IAM also provides data encryption help protect data while in transit and at rest. API Gateway provides access logs and implementation logs to give you visibility into API usage and help you identify security issues.
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ReliabilityRead the Reliability whitepaper
AWS IoT Core enables reliable bi-directional communication between IoT-connected kitchen equipment and AWS services. It can handle a high volume of messages from many pieces of kitchen equipment and reliably routes those messages to AWS for downstream processing and connecting. AWS IoT Core scales to support any number of devices without compromising on reliability, and the built-in retries facilitate communication at scale. Additionally, AWS IoT Greengrass core devices can continue to operate locally if disconnected from the AWS Cloud.
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Performance EfficiencyRead the Performance Efficiency whitepaper
SageMaker enables users to efficiently train and deploy ML models at scale for their IoT telemetry data. Its distributed approach scales model training across multiple nodes to reduce training time. Additionally, its automatic model tuning finds the optimized hyperparameters, and its model inference provides responses with low latency.
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Cost OptimizationRead the Cost Optimization whitepaper
DynamoDB centrally stores all IoT data. The DynamoDB Time to Live (TTL) feature deletes this data from your tables based on configured thresholds. It does this without consuming any write throughput, so you do not need to pay for additional storage, and you can keep your storage costs optimized.
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SustainabilityRead the Sustainability whitepaper
AWS IoT Greengrass enables local compute, messaging, device shadow, and ML inference capabilities on edge devices. Performing compute and inference locally is more energy efficient than sending large amounts of data between local devices and the AWS Cloud. By reducing the need to transmit data to AWS for analysis, you can save network bandwidth and energy consumption, reducing the overall carbon footprint of your workloads.
Implementation Resources
A detailed guide is provided to experiment and use within your AWS account. Each stage of building the Guidance, including deployment, usage, and cleanup, is examined to prepare it for deployment.
The sample code is a starting point. It is industry validated, prescriptive but not definitive, and a peek under the hood to help you begin.
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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.