MLOps Starter — Model Serving and Deployment (3‑Hour Bootcamp)
Learn the essentials of serving, deploying, and monitoring AI models in production. Transition your models from local notebooks to robust, scalable cloud APIs.
Secure Your Spot
Register now to attend the upcoming live interactive Bootcamp.
About this Bootcamp
Building a model is only half the battle. This intensive 3-hour bootcamp is specifically designed for ML engineers, DevOps practitioners, and backend developers who need to bridge the gap between data science and production reliability. You will dive deep into model serving patterns, lightweight FastAPI development, containerization, and the essential CI/CD workflows required to keep AI services operational and scalable.
What You Will Learn
- check_circle Architecting Production APIs: Building high-performance, lightweight model endpoints using FastAPI with request validation.
- check_circle Containerization & Scaling: Implementing Docker best practices to package models for consistent deployment across any environment.
- check_circle Automated CI/CD & Observability: Setting up automated pipelines for model versioning and monitoring health metrics to detect drift.
Bootcamp Outcomes
By the end of this session, you will have a fully functional deployed model endpoint, a professional CI/CD checklist for MLOps, and integrated monitoring hooks to ensure production reliability.
Intensive 3-Hour Agenda
A structured pathway from model environment setup to production-grade deployment.
Serving Foundations & FastAPI
Environment setup followed by an exploration of synchronous vs asynchronous serving patterns and building your first model API.
Containerization & Registry Workflows
Mastering Dockerfile best practices for ML models and publishing images to secure registries for deployment readiness.
CI/CD & Automated Pipelines
Implementing automated builds, testing strategies for AI, and deployment pipelines to ensure reproducible model versioning.
Observability & Hands-on Lab
Integrating logging, health checks, and rollback strategies followed by a live lab to serve and deploy a model.
What Industry Demands
Stay ahead of the curve by mastering the skills companies are hiring for today.
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Production Reliability
Robust serving and scaling to meet strict production SLAs and high-concurrency user demands.
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DevOps Integration
Automated pipelines for reproducible deployments, ensuring model versioning matches software releases.
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Operational Governance
Observability and monitoring to detect performance drift and ensure regulatory compliance.
What Participants Say
"The most practical 3 hours I've spent all year. The FastAPI and Docker integration examples are exactly what we needed at my startup."
Sarah Chen
Machine Learning Engineer"Finally, a bootcamp that focuses on the 'Ops' part of ML. The CI/CD patterns for models were a game changer for our deployment pipeline."
Mark Thompson
DevOps Lead"Excellent pacing. The hands-on lab helped me understand how to turn a pickle file into a production-ready API in minutes."
Anita Rao
Backend DeveloperUpcoming Bootcamp Sessions
LIVE SESSION
MLOps Serving & Deployment - Session A
LIVE SESSION
MLOps Serving & Deployment - Session B
LIVE SESSION
MLOps Serving & Deployment - Session C
Why Join
Pantech Bootcamps