Udemy

AI/LLM Deployment Engineer (Local & Offline)

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  • 132 Students
  • Updated 12/2025
4.8
(06 Ratings)
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Course Information

Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
15 Hour(s) 49 Minute(s)
Language
English
Taught by
Ashish Sharma
Rating
4.8
(06 Ratings)

Course Overview

AI/LLM Deployment Engineer (Local & Offline)

2026 | Run LLM Models Locally and Freely | Run Unhinged LLMs Privately | AI Platforms | Hardware Tweaks | ComfyUI | RAG

/|\ BRAND NEW - 2026 /|\

AI Unchained: Run, Own and Control Your AI/LLMs Locally

- Master Local Inference & Private AI -

Stop Renting Intelligence. Start Architecting It.

A Complete Guide to Hosting, Optimizing, and Building Sovereign AI Systems.

The Era of the "API Wrapper" is Ending.

We are living through a pivotal moment in technology. For the past few years, the standard approach to Artificial Intelligence has been dependency. Developers send their data to a public cloud, pay a toll for every token, and hope their proprietary information remains secure.

But the industry is shifting. The most forward-thinking companies and engineers are realizing that true power lies not in renting intelligence from a giant tech corporation, but in hosting it themselves.

Welcome to the "Engine Room" of the AI Revolution.

This course is not about writing prompts into a chatbox. It is a rigorous, engineering-focused deep dive into the architecture of Local AI. It is designed to transform you from a consumer of third-party APIs into an architect of sovereign, private, and high-performance AI systems.

What You Will Master:

In this comprehensive curriculum, we strip away the abstraction to touch the bare metal. You will learn:

  • The Hardware Reality: How to navigate the constraints of VRAM, memory bandwidth, and compute cores. We demystify the "CPU vs. GPU" debate, teaching you how to run massive intelligence on consumer-grade hardware using advanced quantization techniques.

  • The Optimization Layer: You will master the formats that define modern local inference—GGUF, Safetensors, and ONNX—and learn how to trade precision for speed without losing the model's "soul."

Why This Course?

This is more than a set of tutorials; it is an investment in your Digital Sovereignty.

By the end of this journey, you will possess a rare and lucrative skill set: the ability to deploy secure, private, and cost-efficient AI solutions that do not rely on an internet connection or a credit card. You will understand the legal nuances of open-source licensing, the ethics of data privacy, and the technical engineering required to make AI fast and reliable.

The tools of the future are open, local, and decentralized. The question is: Are you ready to take control?

Join the course today. Let’s set your AI free.

Course Content

  • 18 section(s)
  • 87 lecture(s)
  • Section 1 Introduction
  • Section 2 Hardware, Setup & Environment Preparation
  • Section 3 Speech To Text (STT)
  • Section 4 The AI/LLM Landscape today
  • Section 5 Understanding LLM Foundations
  • Section 6 Advanced LLM Topics
  • Section 7 Local AI in Action: Instant Gratification
  • Section 8 Local AI In Action: The "Command Line" Core (The Engine Room)
  • Section 9 Local AI In Action: The "Frontend" Phase (Building the Cockpit)
  • Section 10 Local AI In Action: The "Power User" & Production Phase
  • Section 11 Local AI In Action: The "Alchemist" Phase (Fine-Tuning)
  • Section 12 Additional Concepts
  • Section 13 Local AI In Action: The 'Architect' Phase (ComfyUI & Visual Programming)
  • Section 14 The Local Developer Experience (Personal Copilot - Local)
  • Section 15 Supercharging Your AI: RAG, Agents, and Tools
  • Section 16 Perception & Voice: Giving AI Eyes and Ears
  • Section 17 Using Multiple GPUs
  • Section 18 Best Wishes and Good Luck

What You’ll Learn

  • Fine Tuning a Model Locally, RAG, Agents, Local Coding Agent in VS Code, Video Generation using ComfyUI, Image Generation using ComfyUI, AI/LLM Deployment Using Numerous Modern Tools, STT using Whiper, Faster Whisper, Distil-Whisper, llama.cpp and Ollama, Advanced Inference Concepts, Using Single GPU as well as Multi GPU set up for Local AI Tools, Vision Language, TTS, OCR, models and more.

Reviews

  • G
    Ganga Ramdas
    5.0

    Goals, tools background experiences are clearly stated. I look forward for a transformation.

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