Edge AI (Artificial Intelligence) refers to the deployment of AI algorithms and models directly on edge devices, such as Internet of Things (IoT) devices, instead of relying on cloud-based servers for processing. This approach enables real-time analysis and decision-making at the edge of the network, reducing latency, enhancing privacy, and conserving bandwidth.
Students learn how to use the Intel® Distribution of OpenVINO™ Toolkit to develop computer vision and deep learning applications. They also learn how to use Intel® DevCloud for the Edge to test performance of deep learning models across distinct hardware types.
- Convert, perform efficient inference with-, deploy, and analyze deep learning models
- Understand distinct hardware types and how they affect deep learning and computer vision
- Optimize and package deep learning models for edge performance
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Exam Details
- Format: Multiple Choice Question
- Questions: 10
- Passing Score: 8/10 or 80%
- Language: English
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Edge AI (Artificial Intelligence) refers to the deployment of AI algorithms and models directly on edge devices, such as Internet of Things (IoT) devices, instead of relying on cloud-based servers for processing.
True
False
_ enables real-time analysis and decision-making at the edge of the network, reducing latency, enhancing privacy, and conserving bandwidth.
Edge AI
Google Cloud
Amazon AWS
Microsoft Azure
_ ensure that the data is properly preprocessed, cleaned, and transformed into a suitable format for training and inference with AI models.
Data Binding
Edge AI
Data Collection and Preprocessing:
Deep Learning
Implement encryption, access controls, and secure communication protocols to ensure the _ of your Edge AI system.
availability
integrity and confidentiality
punctuality
all of the above
__ used to test model performance on various hardware types (CPU, VPU, FPGA, and Integrated GPU).
DevOps
DevTools
DevCloud
None of the above
_ used for the Edge for running deep learning models on the FPGA
Arduino Board
Raspberry Pi
Intel® DevCloud
Node MCU
_ toolkit is used to control your computer pointer using your eye gaze
OpenVINO
OpenCV
Computer Vision
CloseVINO
_ amplifier is used to find and fix hotspots in your application code.
Intel
Nvidia
Asus
VTune
__ is a streaming media analytics framework, based on GStreamer* multimedia framework, for creating complex media analytics pipelines.
Intel® DL Streamer
Gigabyte DL Streamer
AI Streamer
ML Streamer
Media analytics is the analysis of __ streams to detect, classify, track, identify and count objects, events and people. The analyzed results can be used to take actions, coordinate events, identify patterns and gain insights across multiple domains.
audio & video
images
text
All of the above
