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URL: https://www.coursera.org/learn/model-deployment-production-operations-for-mlops

⇱ Deploy ML Models to Production | Coursera

Deploy ML Models to Production

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Deploy ML Models to Production

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Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

4 hours to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

4 hours to complete
Flexible schedule
Learn at your own pace

What you'll learn

  • Build and deploy machine learning models to production environments.

  • Implement MLOps practices for model versioning, tracking, and serving.

  • Secure data and models, ensuring compliance with privacy regulations.

  • Utilize AWS SageMaker and BentoML for cloud-based ML operations.

Details to know

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Recently updated!

March 2026

Assessments

3 assignments

Taught in English

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.

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 videos•Total 41 minutes
  • Introduction to Model Serving•10 minutes
  • Model Drift and Online/Offline Serving•11 minutes
  • Model Deployment and Serving•5 minutes
  • Demo: Model Serving using BentoML - Part 1•5 minutes
  • Demo: Model Serving using BentoML - Part 2•5 minutes
  • Demo: Upgrading Model Versions with BentoML Serving•4 minutes
1 reading•Total 10 minutes
  • How to Reach Out and Engage with the Community•10 minutes
1 assignment•Total 30 minutes
  • Quiz: Model Serving•30 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.

What's included

9 videos3 readings1 assignment

9 videos•Total 48 minutes
  • Monitoring Tools (Prometheus, Grafana, Evidently)•5 minutes
  • Data Privacy and Data Security•10 minutes
  • Data Access Management•7 minutes
  • Data Retention•7 minutes
  • Need of Compliance and GDPR•5 minutes
  • HIPAA Compliance •3 minutes
  • PCI Compliance•4 minutes
  • Compliance Consequences and Penalties•3 minutes
  • Compliance Summary•2 minutes
3 readings•Total 30 minutes
  • Lab: Model Serving using BentoML•10 minutes
  • Quiz - Data Security and Governance •10 minutes
  • Quiz - Data Security and Governance•10 minutes
1 assignment•Total 30 minutes
  • Data Security and Governance •30 minutes

This module provides a deep dive into the AWS SageMaker ecosystem, preparing you to manage the full ML lifecycle on a leading cloud platform.

What's included

3 videos1 assignment

3 videos•Total 15 minutes
  • Overview of SageMaker•7 minutes
  • Core Components of SageMaker•6 minutes
  • MLOps with SageMaker•2 minutes
1 assignment•Total 30 minutes
  • Quiz: Sneak Peek into AWS Sage maker•30 minutes

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Instructor

KodeKloud
21 Courses•38,825 learners

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Frequently asked questions

MLOps combines machine learning, development, and operations to streamline the lifecycle of ML models. It ensures efficient deployment, monitoring, and management of machine learning projects in production, improving data handling and overall operational success.

The course covers data security, privacy, and compliance standards like GDPR. You will learn to implement policies for data access and retention, crucial for managing sensitive data in machine learning projects, especially in sectors like insurance.

You will build a practical deployment project for an insurance claim application. This project involves generating synthetic data, setting up MLflow servers, and integrating models using APIs for online delivery.

Yes, basic programming experience, particularly with Python, is recommended to succeed in this course. Familiarity with data concepts and machine learning fundamentals will also be helpful.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Financial aid available,