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