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Deploy ML Models to Production

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Deploy ML Models to Production

This course is part of Hands-On MLOps Fundamentals for ML Engineers Specialization

Instructor: Mumshad Mannambeth

Included with

Learn more

3 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

4 hours to complete

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

4 hours to complete

Flexible schedule

Learn at your own pace

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

See how employees at top companies are mastering in-demand skills

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Build your subject-matter expertise

This course is part of the Hands-On MLOps Fundamentals for ML Engineers Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 3 modules in this course

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.

Module details

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.

What's included

6 videos1 reading1 assignment

6 videosTotal 41 minutes

Introduction to Model Serving10 minutes
Model Drift and Online/Offline Serving11 minutes
Model Deployment and Serving5 minutes
Demo: Model Serving using BentoML - Part 15 minutes
Demo: Model Serving using BentoML - Part 25 minutes
Demo: Upgrading Model Versions with BentoML Serving4 minutes

1 readingTotal 10 minutes

How to Reach Out and Engage with the Community10 minutes

1 assignmentTotal 30 minutes

Quiz: Model Serving30 minutes

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.