Mastering MLOps: From Model Development to Deployment

Course Overview

• Course Title: Mastering MLOps: From Model Development to Deployment
• Instructor: Vivian Aranha
• Target Audience:
  • Data Scientists
  • Machine Learning Engineers
  • DevOps Professionals
  • AI Enthusiasts
• Prerequisites:
  • Basic Python Programming Skills
  • Fundamentals of Machine Learning
  • Basic Knowledge of Data Science Tools
  • Understanding of Version Control
  • Willingness to Learn Docker and Kubernetes
  • Basic Command-Line Skills
  • Access to a Computer with Internet Connection
  • Curiosity and Problem-Solving Mindset

Curriculum Highlights

• Key Topics Covered:
  • Core concepts, benefits, and evolution of MLOps
  • Differences between MLOps and DevOps practices
  • Setting up a version-controlled MLOps project using Git and Docker
  • Building end-to-end ML pipelines from data preprocessing to deployment
  • Transitioning ML models from experimentation to production environments
  • Deploying and monitoring ML models for performance and data drift
  • Gaining hands-on experience with Docker for ML model containerization
  • Learning Kubernetes basics and orchestrating ML workloads effectively
  • Setting up local and cloud-based MLOps infrastructure (AWS, GCP, Azure)
  • Troubleshooting common challenges in scalability, reproducibility, and reliability
• Key Skills Learned:
  • Managing the entire ML lifecycle
  • Integrating cloud platforms into MLOps pipelines
  • Addressing common challenges in ML deployment
  • Building end-to-end ML pipelines in Python
  • Setting up cloud infrastructure and deploying models locally using Kubernetes
  • Ensuring scalable deployments in production environments

Course Format

• Duration: 2 hours on-demand video
• Format: Self-paced online course
• Resources:
  • Access on mobile and TV
  • Certificate of completion