Machine-Learning Intermediate Premium

Edge Machine Learning for IoT Virtual Internship

In this virtual internship, students will learn how to deploy and optimize machine learning models to run on resource-constrained edge devices like sensors and microcontrollers. They will explore techniques for model compression, quantization, and hardware acceleration to enable efficient inference at the edge. Students will also gain hands-on experience in developing and deploying end-to-end IoT solutions that leverage edge machine learning.

weeks
4 tasks
0 enrolled
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Track price: $49.00

Track Overview

This track provides hands-on experience and real-world projects to build your skills.

Tasks & Milestones

Case Study: Edge ML in Smart Manufacturing

Intermediate

Analyze a real-world case study of how edge machine learning is used in a smart manufacturing environment.

8 hours

Implement Model Quantization

Intermediate

Develop a Python script to quantize a pre-trained machine learning model for deployment on an edge device.

12 hours

Deploy an Edge ML Model to a Raspberry Pi

Intermediate

Package and deploy a machine learning model to run on a Raspberry Pi edge device.

16 hours

Benchmark and Optimize an Edge ML Model

Intermediate

Benchmark an edge ML model, identify performance bottlenecks, and implement optimizations to improve its efficiency.

20 hours

Prerequisites

  • • Basic understanding of machine learning concepts
  • • Familiarity with Python programming

Certificate

Certificate of Completion

Earn a certificate upon successful completion