Edge AI for IoT Developers Certification
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.
Get Edge AI for IoT Developers Certificate from MachineLearning.org.in which you can share in the Certifications section of your LinkedIn profile, on printed resumes, CVs, or other documents.
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
Exam Details :
- Format: Multiple Choice Question
- Questions: 10
- Passing Score: 8/10 or 80%
- Language: English
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.
_ enables real-time analysis and decision-making at the edge of the network, reducing latency, enhancing privacy, and conserving bandwidth.
_ ensure that the data is properly preprocessed, cleaned, and transformed into a suitable format for training and inference with AI models.
Data Collection and Preprocessing:
Implement encryption, access controls, and secure communication protocols to ensure the _ of your Edge AI system.
integrity and confidentiality
all of the above
__ used to test model performance on various hardware types (CPU, VPU, FPGA, and Integrated GPU).
None of the above
_ used for the Edge for running deep learning models on the FPGA
_ toolkit is used to control your computer pointer using your eye gaze
_ amplifier is used to find and fix hotspots in your application code.
__ is a streaming media analytics framework, based on GStreamer* multimedia framework, for creating complex media analytics pipelines.
Intel® DL Streamer
Gigabyte DL 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
All of the above