MLOps Lab: Boeing x WIC x UW
Welcome to the MLOps Lab
We are a group of four graduate students from the University of Washington, Information Management Class of 2026, and this lab is designed to guide you through the core concepts and practical applications of MLOps.
This lab is developed in collaboration with Boeing, working closely with Jared, Uzma, and Geetha, and supported by the Women in Cloud (WIC) team.
Our goal is to provide a hands-on, structured learning experience that bridges academic foundations with real-world MLOps practices used in industry.
Learning Path
Explore the topics below to build your MLOps expertise from foundations to production deployment.
ML Foundations
Learn the fundamentals of machine learning, datasets, and algorithms.
MLOps Pipeline
Build production-ready ML workflows with versioning, testing, and deployment.
Cloud & Infrastructure
Deploy and scale ML systems on Azure with containers and data engineering.
Python Resources
Essential Python learning resources for data science and ML.
Master Python fundamentals, Pandas, NumPy, and Scikit-learn for MLOps workflows.
Getting Started
New to MLOps? Start here to understand why it matters.
Learn why production ML systems need more than just accurate models.
Best Practices
Security, reproducibility, and production-grade workflows.
Learn RBAC, secrets management, monitoring, and cost optimization on Azure.
Our Team
UW Team:
Boeing Mentors: Jared, Uzma, Geetha
Supported by: Women in Cloud (WIC)
This lab uses the NYC Taxi dataset as a real-world example throughout all modules. You’ll learn how to build, deploy, monitor, and maintain ML systems that predict trip duration and fare amounts—just like production systems at scale.