MLOps: Shipping AI to Production
The complete MLOps playbook for AI engineers. CI/CD for ML, model serving, monitoring, drift detection, infrastructure as code, and scaling to millions of users.
or $230 lifetime $799
- Access to all 12 courses
- All future updates
- Certificate of completion
- 30-day money-back guarantee
About This Course
Building an AI model is 20% of the work. Getting it to production and keeping it running reliably is the other 80%. This course covers the full MLOps lifecycle: containerization, CI/CD pipelines for ML, model serving with vLLM and BentoML, drift detection, observability, infrastructure as code with Terraform, and scaling on AWS/GCP. You'll build a production-grade ML platform from scratch.
What You'll Learn
- Containerize ML models and AI applications with Docker and Kubernetes
- Build CI/CD pipelines that automatically test and deploy ML models
- Serve LLMs at scale with vLLM and optimized inference configurations
- Implement model monitoring and automated drift detection
- Design feature stores and data pipelines for ML systems
- Provision and manage cloud ML infrastructure with Terraform
- Build A/B testing and shadow deployment pipelines for models
- Implement cost management and auto-scaling for GPU infrastructure
Who Is This For?
Excellent at model building but want to own the full production lifecycle
Experienced with general infrastructure but new to ML-specific challenges
Building the AI engineering platform for their organization
Prerequisites
- Building AI-Powered Applications with APIs
- Fine-Tuning LLMs recommended
- Basic Docker and Linux knowledge helpful