Introduction to Edge Computing Architecture Quiz

12 Questions

What are the three tiers of the architecture in edge computing?

Data source, intelligence, actionable insights

Where is the training of machine learning models done in edge computing?

In the cloud

What is a key advantage of edge computing in terms of latency?

Reduced latency by avoiding round trips to the cloud

Which area does edge computing not involve in its architecture?

Cloud servers and storage systems

How do edge computing and cloud computing work together?

Form a distributed cloud

What is the primary benefit of Edge Computing in terms of data processing?

Enabling data processing locally and closer to devices

What does the actionable insights tier in edge computing involve?

Sending alerts and taking actions at the edge

What is the primary difference in the role of Cloud and Edge Computing in AI and ML?

Cloud is used for training models, while Edge is used for inferencing

What is a key advantage of Edge Computing in terms of data security and privacy?

Data is processed at the source, maintaining data sovereignty

What is a key characteristic of Edge Computing architecture?

Three layers: data source, Edge Computing layer, and the Cloud

What is a feature of Edge Computing that supports IoT needs?

Bringing services closer to data sources

What enables the viability of Edge Computing?

Innovations in hardware and affordable electronics

Study Notes

Lecture titled "Introduction to Edge Computing" by Dr. Rajiv Mishra from IIT Patna covers various topics including introduction to Edge Computing, building blocks, architecture, and advantages.
Evolution from classical Cloud providing virtual machines to containers for IoT, leading to the development of distributed cloud via Edge computing.
Edge Computing enables data processing locally and brings computation closer to devices, promoting the application of AI and ML.
Cloud used for training models, while Edge for inferencing, reducing latency by avoiding round trips to the cloud.
Data sovereignty is maintained with Edge Computing by keeping data at the source, enhancing security and privacy.
Edge Computing mimics public Cloud platform capabilities, supporting IoT needs by bringing services closer to data sources.
Building blocks of Edge Computing include data ingestion, machine-to-machine brokers, object storage, function as a service, NoSQL databases, stream processing, and machine learning models.
Edge Computing architecture consists of three layers: data source (sensors, databases), Edge Computing layer (algorithms, ML models), and the Cloud (training models, inferencing).
Innovations in hardware and affordable electronics enable moving computation closer to devices, making Edge Computing viable and reducing reliance on the cloud.
Stream processing, function as a service, and machine learning models are now supported at the Edge, enhancing real-time processing and predictive analytics capabilities.- Edge computing allows machine learning models to be trained and run at the edge, close to the data source.
The architecture of edge computing consists of three tiers: data source, intelligence, and actionable insights.
Data sources in edge computing can include IoT devices, sensors, click streams, social media logs, and machine logs.
The intelligence tier in edge computing involves running machine learning models, with training happening in the cloud and inferencing happening at the edge.
The actionable insights tier in edge computing involves sending alerts, populating dashboards, visualizations, and taking actions at the edge.
Edge computing does not follow the traditional three-tier architecture with app servers and databases.
Edge computing offers low latency by avoiding round trips to the cloud and brings cloud-like capabilities closer to the data source.
Edge computing and cloud computing together form a distributed cloud, making the cloud truly distributed.
The basic building blocks of edge computing include data ingestion, storage, and computation at the edge.
The three-tier architecture of edge computing consists of the data source tier, the intelligence tier, and the actionable insights tier.