🤖 Learning path

MLOps & AI Infrastructure

Deploy, scale, and manage LLMs and AI services in production.

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MLOps is the bridge between machine learning and systems engineering. In this path you
will deploy and serve LLMs (Ollama), instrument models with Prometheus metrics and
monitoring, build versioned model-serving architectures with REST prediction endpoints,
and run A/B tests and shadow deployments to roll out new models safely.

What you'll learn

  1. 1. Deploying & Serving LLMs 🧪 Lab · 3 steps · 🔒 Subscriber ○
  2. 2. Model Monitoring & Prometheus Metrics 🧪 Lab · 3 steps · 🔒 Subscriber ○
  3. 3. Model Serving Architectures 🧪 Lab · 3 steps · 🔒 Subscriber ○
  4. 4. Feature Stores & Real-time ML 🧪 Lab · 2 steps · 🔒 Subscriber ○
  5. 5. A/B Testing & Shadow Deployments 🧪 Lab · 3 steps · 🔒 Subscriber ○
  6. 6. Model Registry & Versioning 🧪 Lab · 3 steps · 🔒 Subscriber ○
  7. 7. MLOps - Knowledge Check ❓ Quiz · 🔒 Subscriber ○