The New ML Engineering
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.
The skills that matter today are different from what any university taught you.
Foundation: Modern ML
1. Machine Learning Specialization
Coursera (Andrew Ng)Still the gold standard. Updated in 2024 with modern frameworks. Even experienced engineers find gaps when they revisit the foundations. The mathematical intuition Ng builds is irreplaceable.
Take This Course →2. Fast.ai Practical Deep Learning
fast.aiJeremy Howard’s legendary top-down approach — build working models from day one, then learn theory. Vision, NLP, tabular data, deployment. If Ng is the foundation, fast.ai is the practical muscle.
Take This Course →Advanced: Production AI Systems
7. AI Agents for ML Engineers
DeepLearning.AIMulti-step agents for automated model evaluation, data pipeline agents, self-improving systems. The frontier of ML engineering isn’t building better models — it’s building agents that improve models autonomously.
Take This Course →8. IA y Machine Learning (Ruta Completa)
PlatziLa ruta más completa en español para ML. Desde Python hasta deep learning, NLP, computer vision, y deployment. 50+ horas con proyectos reales.
Take This Course →9. Stanford CS229: Machine Learning
YouTube (Stanford)The full Stanford ML course, free on YouTube. Deeper math and theory than anything else here. If you can work through it, you’ll have a stronger foundation than 95% of ML engineers.
Take This Course →What Ruzora Looks For in ML Engineers
💡 Ruzora's take: Our clients need ML engineers for AI features — not just traditional data science. We vet for:
1. Can you build a RAG system from scratch for a real use case?
2. Have you fine-tuned a model and rigorously evaluated results?
3. Can you deploy and monitor a model in production?
4. Can you build AI features with proper error handling and cost optimization?
Start here: Software dev → ML: take #1 and #2. Already ML: go to #3 and #4. Advanced: focus on #7 (agents).
Key Vocabulary
/ Vocabulario ClaveAdapting a pre-trained model to specific data
“We fine-tuned the model on our customer data.”
Retrieval-Augmented Generation — combining LLMs with external data
“Our RAG system answers questions using company docs.”
Making a model available for production use
“Model deployment includes monitoring and scaling.”
Comprehension Check
/ Verificación de Comprensión1. What does RAG stand for?
2. What is fine-tuning?
