When will I receive my Course Certificate?

If you complete the course successfully, your electronic Course Certificate will be added to your Accomplishments page - from there, you can print your Course Certificate or add it to your LinkedIn profile.

Why can’t I audit this course?

This course is currently available only to learners who have paid or received financial aid, when available.

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.

Learn MLOps for Machine Learning

Grow your skills with Coursera Plus for $239/year (usually $399). Save now.

Learn MLOps for Machine Learning

Instructors: Pearson

Included with

Learn more

1 module

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

7 hours to complete

Flexible schedule

Learn at your own pace

1 module

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

7 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Capitalize on MLOps as an emerging field. Data-focused companies are looking for engineers with these skill sets.
Build a basic MLOps pipeline from scratch with open-source tools - take a working template with you for your own projects.
Take ChatGPT into account to provide a practical bridge for engineers and DevOps teams.

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

5 assignments

Taught in English

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

Learn more about Coursera for Business

logos of Petrobras, TATA, Danone, Capgemini, P&G and L'Oreal

There is 1 module in this course

When teams are working with machine learning models, changing features, different data sets, new algorithms, and unique computing resources all influence a machine learning model's performance. Tracking all of these items can be complicated. With tools such as DVC, MLFlow, AWS, you can meet the challenge. Milecia McGregor demonstrates how to use MLOps tools to improve machine learning and automate some of the steps in the process.

This module introduces MLOps for machine learning engineers, covering the end-to-end pipeline from data collection to production deployment. Learners explore data handling and versioning, model creation and experiment tracking, and best practices for deploying and monitoring models in production. Through practical workflows and industry-standard tools, the course emphasizes automation, reproducibility, and maintaining robust ML systems, equipping participants to apply MLOps principles to real-world projects.

What's included

35 videos5 assignments

35 videos Total 250 minutes

Course Introduction 2 minutes
Learning objectives 1 minute
Gather the data 8 minutes
Analyze the data 8 minutes
Prepare the data 8 minutes
Train a model 8 minutes
Evaluate the model 8 minutes
Validate the model 7 minutes
Deploy the model 8 minutes
Monitor the model 7 minutes
Learning objectives 1 minute
Determine what the data sources are 5 minutes
Create ETL pipelines to compile the data 10 minutes
Understand the data schema with respect to the model 5 minutes
Identify data that can be used for the model 5 minutes
Perform feature engineering 10 minutes
Version the data with DVC 15 minutes
Make multiple data sets 4 minutes
MLOps best practices for data 6 minutes
Learning objectives 1 minute
Use common Python libraries 10 minutes
Code versioning with Git 10 minutes
Perform hyperparameter tuning 11 minutes
Track experiments with MLFlow 10 minutes
Track experiments with DVC 10 minutes
Evaluate the models 10 minutes
Learning objectives 1 minute
Decide the best deployment method 11 minutes
Test on pre-production environments 10 minutes
Deploy to production 5 minutes
Monitor the model for drift 10 minutes
Validate the pipeline flow 11 minutes
Automation points in MLOps 9 minutes
Set up redeploy pipeline 4 minutes
Learn MLOps for Machine Learning: Summary 2 minutes

5 assignments Total 150 minutes

Learning the MLOps Pipeline Quiz 30 minutes
Handling the Data Quiz 30 minutes
Creating a Model Quiz 30 minutes
Working with Production Models Quiz 30 minutes
End of Course Assessment 30 minutes

Instructors

Pearson

268 Courses 49,150 learners

Offered by

Pearson

Explore more from Machine Learning

Google Cloud
Machine Learning Operations (MLOps): Getting Started
Course
Duke University
MLOps | Machine Learning Operations
Specialization
Duke University
MLOps Platforms: Amazon SageMaker and Azure ML
Course
Duke University
DevOps, DataOps, MLOps
Course

Why people choose Coursera for their career

Felipe M.

Learner since 2018

"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."

Jennifer J.

Learner since 2020

"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."

Larry W.

Learner since 2021

"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."

Chaitanya A.

"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."

Open new doors with Coursera Plus

Unlimited access to 10,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription

Learn more

Advance your career with an online degree

Earn a degree from world-class universities - 100% online

Explore degrees

Join over 3,400 global companies that choose Coursera for Business

Upskill your employees to excel in the digital economy

Learn more

Frequently asked questions

Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.

If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.

Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.