MLOps & AI Infrastructure
Deploy and Scale AI Systems
Moving AI from notebooks to production requires a different skillset. This workshop covers the complete MLOps lifecycle—from model registry to deployment patterns to monitoring for drift. Leave with hands-on experience deploying models with proper CI/CD.
What You'll Learn
Full Curriculum
Detailed breakdown of workshop modules and timing
MLOps Fundamentals
DevOps vs MLOps, ML lifecycle management, team structures and responsibilities. Understand the unique challenges of ML in production.
Model Registry & Versioning
MLflow, model cards, experiment tracking, and reproducibility. Build a systematic approach to model management.
Deployment Patterns
Batch vs real-time inference, serverless options, containerization, and GPU allocation strategies.
Hands-on Deployment
Deploy a model with a complete CI/CD pipeline. Implement blue-green deployments and canary releases for ML.
Monitoring & Observability
Model drift detection, performance metrics, alerting strategies, and debugging production ML systems.
Scaling & Cost Management
Auto-scaling strategies, spot instances for training, cost attribution, and resource optimization.
Workshop Outcomes
What you'll walk away with
Who This Workshop Is For
DevOps engineers, platform engineers, ML engineers, SREs
Interested in MLOps & AI Infrastructure?
Tell us about your training needs and we'll provide a custom program recommendation.
All programs are custom-tailored to your organization's specific goals and requirements.
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