MLOps
Deploy AI at scale. Learn model serving, monitoring, versioning, and production best practices.
Prerequisites
Complete Level 7: AI Agents
🎯What You'll Learn
- ✓Model deployment and serving strategies
- ✓Monitoring model performance in production
- ✓ML pipelines and workflow automation
- ✓Model versioning and experiment tracking
- ✓A/B testing and model evaluation
💪Skills You'll Gain
🏆Learning Outcomes
📖Interactive Modules (10)
Model Deployment Pipeline
Understand ML model deployment pipeline, serving, and production infrastructure.
API Design for ML Models
Learn REST API design patterns for exposing machine learning models to applications.
Model Serving Strategies
Master model serving strategies: batch, real-time, streaming inference for production.
A/B Testing for ML
Learn A/B testing methodologies for evaluating ML models in production environments.
Monitoring & Observability
Implement monitoring and observability for ML systems, tracking performance and data drift.
Model Versioning
Master model versioning, experiment tracking with MLflow, and model registry.
Scaling Inference
Learn techniques to scale ML inference: batching, caching, load balancing.
Edge AI Deployment
Deploy AI models to edge devices: smartphones, IoT, and resource-constrained environments.
Quantization Techniques
Understand model quantization to reduce size and increase inference speed without sacrificing accuracy.
Knowledge Distillation
Learn knowledge distillation, training smaller models to mimic larger teacher models.