Comprehensive Guide for Running IOT Systems -AWS GreenGrass!
Comprehensive Guide for Running IOT Systems -AWS GreenGrass!, available at $34.99, has an average rating of 2.75, with 46 lectures, based on 43 reviews, and has 1166 subscribers.
You will learn about Guide for walking through and deploying AWS Greengrass to integrate it with other services. This course is ideal for individuals who are Anyone who is interested in AWS GreenGrass & Running IOT systems It is particularly useful for Anyone who is interested in AWS GreenGrass & Running IOT systems.
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Summary
Title: Comprehensive Guide for Running IOT Systems -AWS GreenGrass!
Price: $34.99
Average Rating: 2.75
Number of Lectures: 46
Number of Published Lectures: 46
Number of Curriculum Items: 46
Number of Published Curriculum Objects: 46
Original Price: $24.99
Quality Status: approved
Status: Live
What You Will Learn
- Guide for walking through and deploying AWS Greengrass to integrate it with other services.
Who Should Attend
- Anyone who is interested in AWS GreenGrass & Running IOT systems
Target Audiences
- Anyone who is interested in AWS GreenGrass & Running IOT systems
AWS Greengrass is software that lets you run local compute, messaging, data caching, sync, and ML inference capabilities for connected devices in a secure way. With AWS Greengrass, connected devices can run AWS Lambda functions, keep device data in sync, and communicate with other devices securely – even when not connected to the Internet. Using AWS Lambda, Greengrass ensures your IoT devices can respond quickly to local events, use Lambda functions running on Greengrass Core to interact with local resources, operate with intermittent connections, stay updated with over the air updates, and minimize the cost of transmitting IoT data to the cloud.
ML Inference is a feature of AWS Greengrass that makes it easy to perform machine learning inference locally on Greengrass Core devices using models that are built and trained in the cloud.
AWS Greengrass seamlessly extends AWS to devices so they can act locally on the data they generate, while still using the cloud for management, analytics, and durable storage. With Greengrass, you can use familiar languages and programming models to create and test your device software in the cloud, and then deploy it to your devices. AWS Greengrass can be programmed to filter device data and only transmit necessary information back to the cloud. AWS Greengrass authenticates and encrypts device data at all points of connection using the security and access management capabilities of AWS IoT Core. This way, data is never exchanged between devices when they communicate with each other and the cloud, without proven identity.
Benefits :
Respond to Local Events in Near Real-time
AWS Greengrass devices can act locally on the data they generate so they can respond quickly to local events, while still using the cloud for management, analytics, and durable storage. The local resource access feature allows Lambda functions deployed on Greengrass Core devices to use local device resources like cameras, serial ports, or GPUs so that device applications can quickly access and process local data.
Operate Offline
AWS Greengrass lets connected devices operate even with intermittent connectivity to the cloud. Once the device reconnects, Greengrass synchronizes the data on the device with AWS IoT Core, providing seamless functionality regardless of connectivity.
Secure Communication
AWS Greengrass authenticates and encrypts device data for both local and cloud communications, so that data is never exchanged between devices and the cloud without proven identity. Greengrass uses the same security and access management you are familiar with in AWS IoT Core, with mutual device authentication and authorization, and secure connectivity to the cloud
Simplified Device Programming with AWS Lambda
AWS Greengrass uses the same AWS Lambda programming model you use in the cloud, so you can develop code in the cloud and then deploy it seamlessly to your devices. Greengrass lets you execute Lambda functions locally, reducing the complexity of developing embedded software
Reduce the Cost of Running IoT Applications
With AWS Greengrass you can program the device to filter device data locally and only transmit the data you need for your applications to cloud. This reduces the amount of raw data transmitted to the cloud and lowers cost, and increases the quality of the data you send to the cloud so you can achieve rich insight at a lower cost.
Use Cases
AWS Greengrass ML Inference can be deployed on connected devices like security cameras, traffic cameras, body cameras, and medical imaging equipment to help them make predictions locally. With AWS Greengrass ML Inference, you can deploy and run ML models like facial recognition, object detection, and image density directly on the device. For example, a traffic camera could count bicycles, vehicles, and pedestrians passing through an intersection and detect when traffic signals need to be adjusted in order to optimize traffic flows and keep people safe.
Retail and Hospitality
Retailers, cruise lines, and amusement parks are investing in IoT applications to provide better customer service. For example, you can run object detection models at amusement parks to keep track of visitor count. Cameras locate the visitors and maintain a running headcount locally without having to send massive amounts of video feed to the cloud, which is often a challenge due to limited internet bandwidth at parks. This solution can predict wait times at popular theme park rides and help improve the customer experience.
Security
Security camera manufacturers are looking for new ways to make devices more intelligent and automate their threat detection capabilities. AWS Greengrass ML Inference can help improve the capabilities of security cameras. Greengrass enabled cameras can continuously scan premises to look for a change in the scene, such as an incoming visitor, and send an alert. The cameras are able to quickly perform scene detection analysis locally and send data to the cloud only when required, e.g., for additional analysis to identify whether a visitor is a family member.
Precision Agriculture
The agriculture industry is going through two major disruptions. First, the world’s population continues to grow causing the demand for food to outweigh the output. Second, climate change is resulting in unpredictable weather conditions, affecting crop yields. AWS Greengrass ML Inference can help transform agriculture practices and deliver new value to customers. Greengrass-powered cameras installed in greenhouses and farms can process images of plants, crops, and data from sensors in the soil to not only detect environmental anomalies such as change in temperature, moisture, and nutrition level, but also trigger alerts.
Predictive Industrial Maintenance
As pricing pressure increases on manufacturers, they are looking for newer ways to help increase operational efficiency on factory floors. Delays in detecting issues on the manufacturing assembly line can lead to a waste of time and resources. AWS Greengrass ML Inference can help you in early detection of faulty equipment and issues on the factory floor. Greengrass-powered industrial gateways can continuously monitor the sensor data (e.g., vibrations, noise-level), predict anomalies, and take relevant actions such as send alerts or shut-off the power to minimize losses.
Course Curriculum
Chapter 1: Introduction
Lecture 1: Introduction
Lecture 2: What is AWS GreenGrass ?
Lecture 3: AWS GreenGrass group
Lecture 4: Devices in AWS GreenGrass
Lecture 5: SDK
Lecture 6: GGC V1.1.0
Lecture 7: GGC V1.0.0
Lecture 8: GG supported platforms and requirement
Chapter 2: Getting Started
Lecture 1: GG getting started
Lecture 2: GG setting Raspberry Pi
Lecture 3: GG Amazon EC2
Lecture 4: GG Other Devices
Lecture 5: GG Module 2- AWS on AWS IoT
Lecture 6: Start AWS GG on Core Device
Chapter 3: Green Grass Config
Lecture 1: GG config-json summary
Lecture 2: GG Lambda function
Lecture 3: GG Configure Lambda function
Lecture 4: cloud configurations
Chapter 4: lambda function
Lecture 1: configure long lived Lambda
Lecture 2: GG test long lived lambda function
Lecture 3: GG Create AWS IoT DEvice in Greengrass Group
Lecture 4: GG Install AWS IoT Device for Python
Chapter 5: Green Grass Communication
Lecture 1: GG Communication
Lecture 2: GG Mod 5 Config Devices & Download Files
Lecture 3: GG Test Communications Device Syncs Enabled-Disabled
Lecture 4: Mod 6 Config IAM Roles & Create Config Lambda Function
Lecture 5: GG Test Communications
Chapter 6: GG OTA Updates of AWS
Lecture 1: GG OTA Updates of AWS – Greengrass OTA Agent
Lecture 2: GG integration with Init System
Lecture 3: GG Reset Deplyments
Lecture 4: GG Configure local resource access
Lecture 5: GG -add the Lambda function in group
Lecture 6: GG Configure Local Resource Access using AWS Step 1,2,3
Lecture 7: GG Steps 4,5,6, test local resource access #487
Lecture 8: GG Perfom Machine Learning Interface #488
Lecture 9: GG Requirements for Machine Learning #489
Lecture 10: GG how to config ML inference using AWS#490
Lecture 11: GG Step 5 and 6 #495
Lecture 12: GG Steps 7 and 8 and Test Inference App #496
Lecture 13: GG Configuring an NVIDIA Jetson TX2 and GG Discovery RESTful API #497
Lecture 14: GG Use gg OPCUA to Communicate #498
Lecture 15: GG AWS gg Security #500
Lecture 16: GG Device Connection, gg Messaging, MQTT Core Server, AWS gg Cipher Suites #501
Lecture 17: GG Monitoring with AWS gg Logs #502
Lecture 18: GG Logging Limitations and Troubleshooting AWS gg Applications #503
Lecture 19: GG Troubleshooting (End Part) #504
Instructors
-
XEH Academy
Former Instructor@ Microsoft,Cisco, GE, HP & JP Morgan Chase
Rating Distribution
- 1 stars: 16 votes
- 2 stars: 5 votes
- 3 stars: 6 votes
- 4 stars: 7 votes
- 5 stars: 9 votes
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