Production AI Engineering
The most advanced step: training and fine-tuning models, serving and deploying them, and the MLOps fundamentals behind monitoring and operating AI systems in production.
4 modules · 12 lessons
01
Training and Fine-Tuning
- Training pipelines end to end
- Fine-tuning pretrained models
- Working with limited compute and data
02
Serving and Deployment
- Serving models with FastAPI or a managed endpoint
- Containerizing with Docker and deployment strategies
- Versioning models safely
03
MLOps Fundamentals
- Monitoring models in production
- Detecting and responding to drift
- Building a repeatable ML pipeline
04
Optimization
- Latency and cost tradeoffs
- Scaling model serving
- Continuous improvement of a live system
Before and after this course.
Before you begin
- Completion of Machine Learning in Practice or equivalent experience
- Working familiarity with Python
- Basic familiarity with cloud/container concepts is helpful
What this actually builds
- Deploy a model as a working API
- Build a basic monitoring setup for a production model
- Understand the MLOps lifecycle end to end
Certificate of Completion
Earn recognition upon course completion
Receive a certificate upon meeting the program's completion criteria. Add it to LinkedIn or your resume as a record of what you built.
Certificate of Completion
Production AI Engineering
- Shareable on LinkedIn and social media
- Verifiable by employers and institutions
- Includes course completion details and skills
Reflects real work
Tied to modules and projects you actually complete
Completion-based
Issued only when the completion criteria are met
Shareable
Add it to LinkedIn or your resume