Deploy ML Models to Production

Overview

This comprehensive course is designed for aspiring MLOps engineers and data scientists looking to bridge the gap between experimental notebooks and robust production environments. You will begin by establishing a strong foundation in model development, exploring the hardware essentials of CPUs and GPUs, and mastering hyperparameter tuning. The curriculum moves rapidly into industrial-grade experimentation using MLflow, where you will learn to track parameters, manage model artifacts, and control versioning through hands-on labs. The second half of the course focuses on real-world application through a specialized project: building a deployment pipeline for an Insurance Claim application. You will gain practical experience generating synthetic data, setting up dedicated MLflow servers, and utilizing BentoML for high-performance model serving. By upgrading a standard Flask application to interact with a professional serving infrastructure, you will master the art of online model delivery. This course ensures you leave with the technical confidence to register, deploy, and manage machine learning models in a live operational setting.

Syllabus

Model Serving

This module focuses on the transition of machine learning models from static files to live, scalable services. You will explore the differences between online and offline serving architectures and learn to handle model drift to ensure long-term accuracy. By the end of this module, you will be proficient in using BentoML to package, deploy, and upgrade model versions in a production environment.

Data Security and Governance

This module covers the legal and ethical framework of MLOps, focusing on data privacy, security, and global compliance standards like GDPR and HIPAA. You will learn to manage data access and retention policies to protect sensitive information.