Practical TinyMLOps

Practical TinyMLOps: How to build an MLOps System for TinyML

Preface

Acknowledgements

Introduction to TinyMLOps

What is MLOps
What is TinyML
MLOps Tools and Techniques
What is TinyMLOps?

Fundamentals of TinyMLOps

MLOps for TinyML
Need for TinyMLOps
TinyMLOps Challenges
Tools for TinyMLOps
A Typical TinyMLOps Pipeline
Hardware for TinyMLOps

Data Engineering

Data Challenges
Data Collection
Preprocessing Data

Training Models for TinyML

Training Models for TinyML
Challenges in Training TinyML Models
Techniques for Training TinyML Models
Tools for Training TinyML Models
Pipeline for Training TinyML Models

Optimizing Models for TinyML

Optimizing Models for TinyML
Challenges in Optimizing TinyML Models
Techniques for Optimizing TinyML Models
Tools for Optimizing TinyML Models
Pipeline for Optimizing TinyML Models

Benchmarking TinyML Systems

What is Benchmarking
Challenges in Benchmarking TinyML Systems
How to Benchmark TinyML Systems
Pipeline for Benchmarking TinyML Systems

Deploying TinyML Models

Deploying TinyML Systems
Challenges in Deploying TinyML Systems
How to Deploy TinyML Systems
Pipeline for Deploying TinyML Systems

Monitoring and Updating TinyML Systems

Monitoring and Updating TinyML Systems
Challenges in Monitoring and Updating TinyML Systems
Techniques for Monitoring and Updating TinyML Systems
Pipeline for Monitoring and Updating TinyML Systems

Security and Robustness of TinyML Systems

Secure and Robust TinyML Systems
Challenges in Designing Secure and Robust TinyML Systems
Creating Secure and Robust TinyML Systems
Architecture and Pipeline

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Data Collection

Data Collection¶

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Data Challenges

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Preprocessing Data

By Soham Chatterjee
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