Machine learning engineering in 2026 is fundamentally different from 2023. Foundation models, RAG, and production LLM systems have shifted the field from training from scratch to fine-tuning, orchestrating, and deploying large-scale AI systems.
Si eres ingeniero de ML, las habilidades que importan hoy son diferentes de lo que cualquier universidad te enseñó.
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Foundation: Modern ML Engineering
1. Machine Learning Specialization (Andrew Ng)
Platform: Coursera | Link: https://www.coursera.org/specializations/machine-learning-introduction
Cost: Free to audit | Duration: 3 months | Language: English + Spanish subtitles
Still the gold standard for ML fundamentals. Updated in 2024.
2. Fast.ai Practical Deep Learning
Platform: fast.ai | Link: https://course.fast.ai/
Cost: Free | Duration: 7 weeks | Language: English
Build working models from day 1, then learn theory. Vision, NLP, tabular data, deployment.
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LLM Engineering (The New Core)
3. LLM Fine-Tuning (DeepLearning.AI)
Platform: DeepLearning.AI | Link: https://www.deeplearning.ai/short-courses/finetuning-large-language-models/
Cost: Free | Duration: 1 hour | Language: English
Fine-tune Llama and Mistral with LoRA/QLoRA. Critical skill for 2026.
4. RAG Systems (DeepLearning.AI)
Platform: DeepLearning.AI | Link: https://www.deeplearning.ai/short-courses/building-evaluating-advanced-rag/
Cost: Free | Duration: 1 hour | Language: English
Vector databases, chunking, retrieval evaluation, advanced RAG patterns. How most production AI works.
5. Production ML with MLOps (Google Cloud)
Platform: Coursera | Link: https://www.coursera.org/specializations/machine-learning-engineering-for-production-mlops
Cost: Free to audit | Duration: 4 months | Language: English
Data pipelines, model serving, monitoring, CI/CD for ML.
6. Hugging Face NLP Course
Platform: Hugging Face | Link: https://huggingface.co/learn/nlp-course
Cost: Free | Duration: Self-paced | Language: English
Tokenization, fine-tuning, inference optimization. Every ML engineer needs this.
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Advanced: Production AI Systems
7. AI Agents for ML Engineers
Platform: DeepLearning.AI | Link: https://www.deeplearning.ai/short-courses/ai-agents-in-langgraph/
Cost: Free | Duration: 1.5 hours | Language: English
Multi-step agents for automated model evaluation, data pipeline agents, self-improving systems.
8. IA y Machine Learning — Platzi
Platform: Platzi | Link: https://platzi.com/ruta/inteligencia-artificial/
Cost: ~$24/month | Duration: 50+ hours | Language: Español
La ruta más completa en español para ML. Desde Python hasta deep learning, NLP, y deployment con proyectos reales.
9. Stanford CS229 (Free)
Platform: YouTube | Link: https://www.youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU
Cost: Free | Duration: 20 lectures | Language: English
Full Stanford ML course. Deeper math and theory for understanding WHY models work.
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What Ruzora Looks For in ML Engineers
1. Can you build a RAG system from scratch for a real use case?
2. Have you fine-tuned a model on custom data and evaluated results?
3. Can you deploy and monitor a model in production?
4. Can you build AI features with Claude/OpenAI APIs with proper error handling and cost optimization?
Start here: Software dev transitioning to ML → #1 and #2. Already ML → #3 and #4. Advanced → #7 (agents).
