Development

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
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