MLOps for Edge Devices: Optimizing Models for Deployment at the Edge Virtual Internship
In this virtual internship, students will learn how to build and deploy machine learning models on edge devices using techniques like model compression, quantization, and hardware acceleration. They will gain hands-on experience with MLOps tools and workflows, enabling them to optimize models for deployment at the edge. By the end of the internship, students will be able to build and deploy efficient, high-performing ML models on a variety of edge devices, preparing them for careers in MLOps, edge computing, and embedded systems.
Track Overview
Tasks & Milestones
Edge Device Research
IntermediateIn this task, students will research and compare different edge device architectures, hardware specifications, and use cases.
Model Compression with Pruning
IntermediateIn this task, students will implement model pruning to compress a pre-trained model for deployment on an edge device.
Model Quantization
IntermediateIn this task, students will explore model quantization techniques to further optimize a machine learning model for deployment on an edge device.
Hardware Accelerator Evaluation
IntermediateIn this task, students will research and evaluate different hardware accelerators for edge devices, and assess their suitability for machine learning workloads.
Integrating Hardware Acceleration
IntermediateIn this task, students will integrate hardware acceleration into their MLOps workflow for edge deployments.
End-to-End MLOps Pipeline
IntermediateIn this final project, students will design and implement an end-to-end MLOps pipeline for edge deployments.
Prerequisites
- • Experience with machine learning and model development
- • Familiarity with Python and common ML libraries
- • Basic understanding of software engineering and deployment
Certificate
Certificate of Completion
Earn a certificate upon successful completion