AI/ML Builder’s Companion Site

“From cost-conscious builders to cloud-ready engineers—this is your blueprint for real-world AI.”

Live Site: https://mcklay.github.io/MLops-Project-Handbook/
Author: Clay Mark Sarte
Built with MkDocs Material | Powered by real-world AI projects & MLOps experience

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📘 What This Is

The AI/ML Builder’s Companion Site is a dual-book documentation project designed to guide engineers, students, and AI developers through:

1. Practical AI/ML Project Development
2. Modern Deployment and Tooling Strategies

Each book is modular, deployment-aware, and grounded in actual project experience—from deploying Hugging Face demos to scaling Dockerized APIs.

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Included Books

📘 Mastering AI/ML Projects on a Budget

A practical guide to building and shipping AI apps using free-tier tools like Hugging Face, Railway, Vercel, and Replicate.

• Build full AI pipelines—from model to UI
• Choose between local or API-based inference
• Deploy backend & frontend on cloud platforms
• Avoid billing pitfalls using smart cost strategies

📘 AI/ML Project Toolkit

A tool-by-tool breakdown of the ecosystem: FastAPI, Gradio, CI/CD, Docker, databases, logging, and more.

• Learn the tools that productionize AI systems
• Build secure and scalable infrastructures
• Understand rate limits, GPU runtimes, auth, and cloud hosting
• Develop a builder’s mindset and ship faster

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Book Structure & Table of Contents

Both books are split into logical, practical parts:

🔹 Mastering AI/ML Projects on a Budget

• Part I: Foundations
• Part II: Project Development (Backend, UI, APIs)
• Part III: Free-Tier Deployment Strategies
• Part IV: Cost-Optimization & API Scaling
• Part V: Builder Roadmap & Templates

🔹 AI/ML Project Toolkit

• Part I: Dev & Deployment Essentials (FastAPI, Docker, CI/CD)
• Part II: AI/ML Tools (Transformers, APIs, Tokenizers)
• Part III: Scaling, Monitoring, Security (Rate Limits, Logs, Auth)
• Part IV: Philosophy & Builder Mindset

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Run the Site Locally

git clone https://github.com/McKlay/MLops-Project-Handbook.git
cd MLops-Project-Handbook
pip install -r requirements.txt
mkdocs serve
# Open: http://127.0.0.1:8000

To deploy via GitHub Pages:

mkdocs gh-deploy

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Contributing

You’re welcome to:

• Open issues for bugs, improvements, or new chapter ideas
• Submit PRs for formatting, typo fixes, or better code examples
• Share your own deployment tips or platform configs

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License

MIT License © Clay Mark Sarte Feel free to learn, fork, and remix—with attribution.

“Every working ML system starts with an idea. This site helps you ship it.”

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