DATA UNIVERSEDescripción detallada del cursoDetailed course description
Contenido en inglés, el idioma en que se imparte el curso.Content in English, the language the course is taught in.
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.
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.
Qué aprenderás en este cursoWhat you'll learn in this course
A quién va dirigidoWho 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.
Requisitos previosPrerequisites
- 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
Programa completo del cursoFull course curriculum
Contenido en inglés, el idioma en que se imparte el curso.Content in English, the language the course is taught in.
01FOUNDATIONS AND CONTEXT3 claseslectures · 18m
- Why Fine-Tuning on Edge7m
- Small Language Models - Recap6m
- What is Fine-Tuning?5m
02EFFICIENT FINE-TUNING TECHNIQUES4 claseslectures · 22m
- Full Fine-Tuning vs. PEFT6m
- LoRA (Low-Rank Adaptation)6m
- QLoRA (Quantized LoRA)5m
- Post-Training Quantization5m
03DATA PREPARATION AND CONFIGURATION3 claseslectures · 19m
- Data Selection and Preparation6m
- Key Hyperparameters6m
- Tools and Frameworks7m
04PRACTICE EXAM 10 + 1 claseslectures
05EDGE DEPLOYMENT STRATEGY4 claseslectures · 25m
- Optimization for Edge Devices7m
- Quantization for Inference7m
- Packaging for Distribution5m
- Validation and Testing on Edge6m
06PRACTICAL CASES3 claseslectures · 16m
- Case 1 - Enterprise Document Classification5m
- Case 2 - Technical Support Assistant6m
- Case 3 - Specialized Content Generation5m
07BEST PRACTICES AND CONCLUSION2 claseslectures · 12m
- Common Pitfalls5m
- Near Future6m
08PRACTICE EXAM 20 + 1 claseslectures
09BONUS: AI Governance Toolkit1 claseslectures · 2m
- BONUS: AI Governance Toolkit2m
Preguntas frecuentes sobre este cursoFrequently asked questions about this course
¿En qué idioma está el curso?What language is the course in?
Se imparte íntegramente en inglés. Todo el material del curso está en ese idioma. Puedes comprobar en Udemy si hay subtítulos disponibles para tu idioma.It is taught entirely in English. All course material is in that language. You can check on Udemy whether subtitles are available for your language.
¿Qué nivel se necesita?What level is required?
Está catalogado como todos los niveles. Los requisitos publicados por el instructor están en la pestaña de visión general, en el idioma del curso.It is listed as all levels. The requirements published by the instructor are in the overview tab, in the course language.
¿Cuánto dura y qué incluye?How long is it and what is included?
1h 52m de vídeo en 20 clases repartidas en 9 secciones (2 tests), más el certificado de finalización de Udemy.1h 52m of video across 20 lectures in 9 sections (2 tests), plus the Udemy certificate of completion.
¿Cuánto tiempo tengo acceso?How long do I have access?
Acceso de por vida en Udemy, con las actualizaciones incluidas y garantía de devolución de 30 días según las políticas de Udemy.Lifetime access on Udemy, with updates included and a 30-day refund guarantee under Udemy policies.
¿Se puede contratar para un equipo o empresa?Can it be arranged for a team or company?
Sí. Impartimos formación in-company partiendo de este contenido, adaptada al sector y al nivel del equipo. Usa el botón de solicitar formación y te contamos opciones.Yes. We deliver in-company training based on this content, adapted to your sector and your team level. Use the request training button and we will walk you through the options.
Valoración de los alumnosStudent ratings
Las opiniones se gestionan y verifican en la plataforma Udemy. Puedes leerlas todas en la página del curso:Reviews are managed and verified on the Udemy platform. You can read them all on the course page: ver opiniones en Udemy →see reviews on Udemy →