AI/LLM Deployment Engineer (Local & Offline)
About This Course
This course focuses on running and deploying large language models locally and offline. It teaches how to use AI models privately without relying on cloud platforms, while also covering deployment tools, hardware optimization, and AI workflows.
Quick Details
- Rating: 4.7 / 5
- Students: 225
- Duration: 16 Hours
- Articles: 2
- Resources: 4 Downloadable Files
- Language: English
- Certificate: Yes
- Access: Lifetime
- Price: ₹399 (Discounted)
What You’ll Learn
- Run and deploy LLM models locally and offline
- Fine-tune AI models on local systems
- Build RAG-based AI workflows
- Create local coding agents inside VS Code
- Generate images and videos using ComfyUI
- Use speech-to-text tools like Whisper and Faster Whisper
- Work with Ollama and llama.cpp
- Understand advanced inference and optimization techniques
- Configure single GPU and multi-GPU AI setups
- Use vision-language models, OCR, TTS, and related AI tools
Key Topics Covered
- Local LLM Deployment
- Offline AI Systems
- RAG Applications
- AI Infrastructure
- GPU Optimization
- ComfyUI Workflows
Why This Course
This course helps learners understand how to run AI systems privately and efficiently on local hardware. It focuses on practical deployment workflows and modern open-source AI tools.
Who Should Take This
- AI enthusiasts exploring local AI setups
- Developers working with LLMs
- Engineers interested in offline AI deployment
- Anyone interested in private AI systems and infrastructure
Final Thoughts
A practical course for learning local and offline AI deployment. It is suitable for learners who want hands-on experience with running LLMs privately using modern AI tools and optimized hardware setups.
Deal Score0
