Is this course really 100% online? Do I need to attend any classes in person?

This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

Can I just enroll in a single course?

Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.

Is financial aid available?

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.

Can I take the course for free?

No, you cannot take this course for free. 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. If you cannot afford the fee, you can apply for financial aid.

MLOps | Machine Learning Operations Specialization

New! Discover how 91% of learners achieved at least one positive career outcome. Learn more.

MLOps | Machine Learning Operations Specialization

Become a Machine Learning Engineer. Level-up your programming skills with MLOps

Instructors: Noah Gift

Included with Coursera Plus

Learn more

4 course series

Get in-depth knowledge of a subject

3.9

(305 reviews)

Advanced level

Recommended experience

6 months at 5 hours a week

Flexible schedule

Earn a career credential

Share your expertise with employers

4 course series

Get in-depth knowledge of a subject

3.9

(305 reviews)

Advanced level

Recommended experience

6 months at 5 hours a week

Flexible schedule

Earn a career credential

Share your expertise with employers

What you'll learn

Master Python fundamentals, MLOps principles, and data management to build and deploy ML models in production environments.
Utilize Amazon Sagemaker / AWS, Azure, MLflow, and Hugging Face for end-to-end ML solutions, pipeline creation, and API development.
Fine-tune and deploy Large Language Models (LLMs) and containerized models using the ONNX format with Hugging Face.
Design a full MLOps pipeline with MLflow, managing projects, models, and tracking system features.

Overview

This comprehensive course series is perfect for individuals with programming knowledge such as software developers, data scientists, and researchers. You'll acquire critical MLOps skills, including the use of Python and Rust, utilizing GitHub Copilot to enhance productivity, and leveraging platforms like Amazon SageMaker, Azure ML, and MLflow. You'll also learn how to fine-tune Large Language Models (LLMs) using Hugging Face and understand the deployment of sustainable and efficient binary embedded models in the ONNX format, setting you up for success in the ever-evolving field of MLOps

Through this series, you will begin to learn skills for various career paths:

1. Data Science - Analyze and interpret complex data sets, develop ML models, implement data management, and drive data-driven decision making.

2. Machine Learning Engineering - Design, build, and deploy ML models and systems to solve real-world problems.

3. Cloud ML Solutions Architect - Leverage cloud platforms like AWS and Azure to architect and manage ML solutions in a scalable, cost-effective manner.

4. Artificial Intelligence (AI) Product Management - Bridge the gap between business, engineering, and data science teams to deliver impactful AI/ML products.

What’s included

Shareable certificate

Add to your LinkedIn profile

Taught in English

61 practice exercises

Advance your subject-matter expertise

Learn in-demand skills from university and industry experts
Master a subject or tool with hands-on projects
Develop a deep understanding of key concepts
Earn a career certificate from Duke University

Specialization - 4 course series

Explore and practice your MLOps skills with hands-on practice exercises and Github repositories.

1. Building a Python script to automate data preprocessing and feature extraction for machine learning models.

2. Developing a real-world ML/AI solution using AI pair programming and GitHub Copilot, showcasing your ability to collaborate with AI.

4. Creating web applications and command-line tools for ML model interaction using Gradio, Hugging Face, and the Click framework.

3. Implementing GPU-accelerated ML tasks using Rust for improved performance and efficiency.

4. Training, optimizing, and deploying ML models on Amazon SageMaker and Azure ML for cloud-based MLOps.

5. Designing a full MLOps pipeline with MLflow, managing projects, models, and tracking system features.

6. Fine-tuning and deploying Large Language Models (LLMs) and containerized models using the ONNX format with Hugging Face. Creating interactive demos to effectively showcase your work and advancements.

Python Essentials for MLOps

Course 143 hours4.3 (309 ratings)View course

What you'll learn

Work with logic in Python, assigning variables and using different data structures.
Write, run and debug tests using Pytest to validate your work.
Interact with APIs and SDKs to build command-line tools and HTTP APIs to solve and automate Machine Learning problems.

Skills you'll gain

Python Programming, Pandas (Python Package), Command-Line Interface, Software Testing, Object Oriented Programming (OOP), NumPy, Machine Learning, Test Automation, Debugging, Data Structures, Application Programming Interface (API), Scripting, Numerical Analysis, Data Import/Export, MLOps (Machine Learning Operations), Program Development, and Data Manipulation

DevOps, DataOps, MLOps

Course 244 hours4.2 (196 ratings)View course

What you'll learn

Build operations pipelines using DevOps, DataOps, and MLOps
Explain the principles and practices of MLOps (i.e., data management, model training and development, continuous integration and delivery, etc.)
Build and deploy machine learning models in a production environment using MLOps tools and platforms.

Skills you'll gain

MLOps (Machine Learning Operations), CI/CD, Rust (Programming Language), DevOps, Containerization, Serverless Computing, Responsible AI, Tensorflow, Big Data, Cloud Solutions, Docker (Software), Microsoft Copilot, Artificial Intelligence and Machine Learning (AI/ML), PyTorch (Machine Learning Library), Machine Learning, Command-Line Interface, Web Frameworks, and GitHub

MLOps Platforms: Amazon SageMaker and Azure ML

Course 331 hours3.7 (51 ratings)View course

What you'll learn

Apply exploratory data analysis (EDA) techniques to data science problems and datasets.
Build machine learning modeling solutions using both AWS and Azure technology.
Train and deploy machine learning solutions to a production environment using cloud technology.

Skills you'll gain

MLOps (Machine Learning Operations), Microsoft Azure, Amazon S3, Exploratory Data Analysis, Machine Learning Methods, Cloud Engineering, Cloud Solutions, Serverless Computing, Machine Learning, Amazon Web Services, Applied Machine Learning, Data Pipelines, Data Analysis, Python Programming, Artificial Intelligence and Machine Learning (AI/ML), Feature Engineering, Machine Learning Algorithms, and AWS SageMaker

MLOps Tools: MLflow and Hugging Face

Course 426 hours3.7 (59 ratings)View course

What you'll learn

Create new MLflow projects to create and register models.
Use Hugging Face models and datasets to build your own APIs.
Package and deploy Hugging Face to the Cloud using automation.

Skills you'll gain

MLOps (Machine Learning Operations), Containerization, Docker (Software), Application Deployment, Microsoft Azure, CI/CD, Cloud Applications, Application Programming Interface (API), Cloud Computing, GitHub, and Machine Learning Software

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

Instructors

Noah Gift

Duke University

40 Courses198,717 learners

Alfredo Deza

Duke University

29 Courses155,401 learners

Offered by

Duke University

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Frequently asked questions

The course series takes approximately 6 months to complete.

You should have basic Python programming experience, familiarity with computer science concepts, and a strong foundation in mathematics (especially linear algebra and statistics).

The course series is designed to be completed in the order outlined here on this Specialization Description Page.

Note that the Specialization Certificate does not represent official academic credit from the partner institution offering the course. Duke cannot provide a transcript for your completion of the Specialization; however, we encourage you to share your Coursera completion certificate with your employer and community to demonstrate your completion of the course series.

Through this series, you will begin to learn skills for various career paths:

Data Science - Analyze and interpret complex data sets, develop ML models, implement data management, and drive data-driven decision making.
Machine Learning Engineering - Design, build, and deploy ML models and systems to solve real-world problems.
Cloud ML Solutions Architect - Leverage cloud platforms like AWS and Azure to architect and manage ML solutions in a scalable, cost-effective manner.
Artificial Intelligence (AI) Product Management - Bridge the gap between business, engineering, and data science teams to deliver impactful AI/ML products.

MLOps | Machine Learning Operations Specialization

What you'll learn

Overview

Skills you'll gain

Tools you'll learn

What’s included

Advance your subject-matter expertise