Edge AI with SLMs: Fine-Tuning & Local Deployment
Published 2/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 1h 51m | Size: 1.28 GB
What you'll learn
Design and fine-tune small language models (1-7B) specifically for edge and mobile devices, balancing accuracy, size, and latency
Apply LoRA and QLoRA to fine-tune SLMs on consumer GPUs, drastically reducing VRAM needs and training time for real projects
Quantize fine-tuned models (INT8/INT4), convert them to edge-friendly formats, and deploy them on phones, tablets, and Raspberry Pi
Build an end‑to‑end pipeline from data preparation and hyperparameter tuning to on‑device validation, benchmarking, and optimization
Decide when to use prompt engineering, RAG, or fine‑tuning, and justify edge deployment versus cloud APIs for different business use cases
Select the right SLM family (Gemma, Phi, Llama, Mistral) for your constraints in VRAM, hardware, privacy, and on‑device performance
Design high‑quality instruction datasets and splits, avoiding overfitting and catastrophic forgetting in small, specialized models
Package, version, and update on‑device models (monolithic vs modular adapters) for real‑world apps like classification, support bots, and content generation
Requirements
A general understanding of what AI or "ChatGPT‑style" models are is useful, but the course includes a quick conceptual recap so motivated beginners can follow
Access to a computer (Windows, macOS o Linux) where you can install Python and common AI libraries; no need for prior setup experience
Basic Python knowledge (variables, functions, and running simple scripts) is helpful but not strictly required; all code is explained step by step
Description
This course provides a comprehensive technical framework for fine-tuning Small Language Models (SLMs) and deploying them on edge devices.
Moving beyond the hype of massive cloud models, this guide focuses on the engineering reality of running private, offline AI. You will learn the end-to-end methodology to transform general-purpose models (1-7B parameters) into specialized, efficient tools that run directly on user hardware, without depending on internet connectivity or external APIs.
What you will learn
• The Strategic Shift to Edge AI: Understand the architectural trade-offs between Cloud and Edge. We analyze exactly when to move processing to the device to solve issues of latency, data privacy, and recurring cloud costs.
• Small Language Models (SLMs) Deep Dive: A technical breakdown of the SLM landscape (Phi, Gemma, Llama, Mistral) and why their architecture makes them viable for smartphones, tablets, and embedded IoT systems.
• Optimization Techniques (The "How-To"): We deconstruct the core mechanisms of Parameter-Efficient Fine-Tuning (PEFT). You will understand how LoRA and QLoRA work to adapt models using consumer-grade GPUs, and how Quantization (INT4/INT8) reduces model size without destroying performance.
• The Deployment Pipeline: A step-by-step look at the lifecycle of a local model: from dataset preparation and hyperparameter selection to conversion into edge-friendly formats (like GGUF or ONNX).
• Real-World Production Scenarios: We examine concrete case studies including enterprise document classification and offline support assistants to validate how these systems perform regarding memory usage, battery life, and inference speed.
Who is this for: This course is designed for AI architects, technical leads, and engineers who need a clear roadmap and conceptual understanding of how to design, train, and ship on-device AI systems, moving from theory to production-ready strategies.
Who this course is for
University students, bootcamp graduates, and junior developers who already know basic Python and want a practical path into applied AI, without needing to train huge models from scratch.
Non‑technical founders, startup builders, and tech‑savvy professionals who don't write code every day but want a clear, strategic understanding of how fine‑tuned small models can run privately on user devices.
Software engineers, ML engineers, and data scientists who want to fine‑tune and deploy small language models directly on devices instead of relying only on cloud APIs
Backend, mobile, and embedded developers interested in adding on‑device AI features (classification, assistants, automation) to apps running on phones, tablets, or edge hardware.
Technical leads, architects, product managers, and innovation managers who need to evaluate the trade‑offs between cloud AI and on‑device AI for cost, latency, and privacy.
AI/ML practitioners used to working with large cloud models (e.g., via APIs) who now want to learn LoRA, QLoRA, quantization, and edge deployment to modernize their skill set.
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