OpenCourser brand icon
Sign in
We may earn an affiliate commission when you visit our partners.
Course Discontinued We've found some alternatives for you:

Top Active Alternatives

Demystifying Machine Learning Operations (MLOps) is no longer accepting new enrollments. We've matched the highest-rated active courses in this subject:

Read Reviews → Enroll Active
Coursera
Developing Machine Learning Solutions
By Amazon Web Services
Read Reviews → Enroll Active
Read Reviews → Enroll Active
Pluralsight logo

Demystifying Machine Learning Operations (MLOps)

Mohammed Osman

Managing the machine learning process using recommended practices is a must to enable collaboration, tracing, and real-time monitoring. This course will teach you what are the main concerns and issues you need to consider while developing a machine learning model and after deploying it.

Read more

Managing the machine learning process using recommended practices is a must to enable collaboration, tracing, and real-time monitoring. This course will teach you what are the main concerns and issues you need to consider while developing a machine learning model and after deploying it.

Machine Learning is a robust science that can empower the business with unique competitive advantages to address several challenges, such as sales price prediction, customer segment classification, and product recommendation. In this course, Demystifying Machine Learning Operations (MLOps), you’ll learn to implement machine learning operations into your machine learning project. First, you’ll explore how to apply machine learning operations (MLOps) practices for your infrastructure. Next, you’ll discover how machine learning operations (MLOps) during model development. Finally, you’ll learn how to apply machine learning operations (MLOps) after model deployment. When you’re finished with this course, you’ll have the skills and knowledge of machine learning operations needed to manage the MLOps lifecycle of your project.

This course is no longer available. Find something similar by browsing:
Machine Learning Operations (MLOps) Machine Learning Infrastructure Model Development Best Practices Model Deployment Best Practices

What's inside

Syllabus

Course Overview
Understanding Machine Learning Operations
Machine Learning Operations for Infrastructure
Implementing Best Practices for Model Development
Read more

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Teaches best practices for machine learning development and deployment
Covers tools and methods that align with industry standards
Helps learners manage and monitor machine learning models throughout the lifecycle

Save this course

Create your own learning path. Save this course to your list so you can find it easily later.
Save

Reviews summary

Demystifying mlops: a foundational overview

According to students, this course offers a clear and concise overview of the entire MLOps lifecycle, from infrastructure to deployment. Learners found the instructor's explanations lucid, making complex concepts easily digestible. While many appreciated its foundational nature and how it demystified MLOps for them, some noted it serves more as a conceptual introduction rather than a deep dive into specific tooling or extensive hands-on labs. It is highly recommended for professionals seeking an initial understanding of MLOps practices.
Highly relevant for professionals applying MLOps practices in their work.
"I found this course highly relevant for professionals."
"It really demystified MLOps for me and gave me a solid roadmap for applying these practices in my work."
"The information presented is very relevant to current industry practices."
The instructor is praised for their clear explanations and engaging style.
"The instructor explained complex concepts clearly and provided practical examples."
"I found the instructor's teaching style engaging, and the topics flowed logically."
"The instructor is knowledgeable and passionate. The course covers the entire lifecycle, which is crucial."
The course provides an easily digestible and well-structured introduction.
"This course provided a fantastic overview of MLOps from start to finish. The instructor explained complex concepts clearly..."
"Super clear and concise. As someone relatively new to MLOps, this course was a lifesaver. It broke down complicated ideas into easily digestible modules."
"I found the information presented in an easy-to-understand manner. It's an excellent primer for MLOps."
Offers a strong theoretical basis but lacks extensive practical depth.
"Some parts felt a bit high-level, and I wished for more hands-on labs or deeper dives into specific tools."
"While it covers the main concepts, I felt like it just scratched the surface in some areas, particularly concerning specific tooling integrations."
"I left feeling like I had a basic vocabulary but not the skills to truly implement anything. More concrete examples and real-world scenarios would significantly improve it."
"I understand it's more conceptual than practical. If I'm looking for a deep dive into specific tools, this isn't it, but it's great for understanding the 'what' and 'why'."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Demystifying Machine Learning Operations (MLOps) with these activities:
Review programming concepts
Strengthen programming skills in languages commonly used for machine learning, such as Python or R, to ensure a solid foundation for implementing machine learning algorithms.
Browse courses on Programming
Show steps
  • Revisit basic programming concepts
  • Practice writing and executing code snippets
  • Complete coding challenges or exercises
Review machine learning concepts
Review the fundamentals of machine learning, including supervised and unsupervised learning, model selection, and evaluation.
Browse courses on Machine Learning
Show steps
  • Read through introductory materials on machine learning
  • Solve practice problems on supervised and unsupervised learning
  • Identify and research different machine learning algorithms
Practice implementing machine learning algorithms
Gain hands-on experience implementing various machine learning algorithms to solidify your understanding.
Show steps
  • Use online coding platforms or tutorials to practice coding machine learning algorithms.
  • Work through practice problems or assignments that require you to implement machine learning models.
  • Participate in online coding challenges or competitions to test your skills.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Participate in study groups or discussions
Engage with other learners to discuss concepts, share knowledge, and resolve challenges, fostering collaboration and improving comprehension.
Browse courses on Machine Learning
Show steps
  • Join or form a study group with peers
  • Actively participate in discussions and ask questions
  • Share knowledge and insights with others
Practice building machine learning models
Apply machine learning techniques to real-world datasets to gain hands-on experience and improve model-building skills.
Browse courses on Model Building
Show steps
  • Choose a dataset and define the problem statement
  • Explore and preprocess the data
  • Train and evaluate different machine learning models
  • Fine-tune and optimize the best performing model
Write a blog post or article on machine learning
Summarize key concepts, share practical insights, or discuss current trends in machine learning to reinforce understanding and improve communication skills.
Browse courses on Machine Learning
Show steps
  • Choose a specific topic within machine learning
  • Research and gather information on the topic
  • Write a well-structured and informative blog post or article
  • Share the content on relevant platforms
Explore advanced machine learning techniques
Delve into specialized machine learning areas such as deep learning, natural language processing, or time series analysis to expand knowledge and skills.
Browse courses on Advanced Machine Learning
Show steps
  • Identify an area of interest within advanced machine learning
  • Find online tutorials or courses on the chosen topic
  • Complete the tutorials and practice exercises
Build a machine learning project
Apply machine learning concepts to solve a real-world problem, gain hands-on experience, and develop a comprehensive project portfolio.
Show steps
  • Define the project scope and objectives
  • Gather and preprocess data
  • Build, train, and evaluate machine learning models
  • Deploy the model and track its performance
  • Document and present the project outcomes

Career center

Learners who complete Demystifying Machine Learning Operations (MLOps) will develop knowledge and skills that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineers are responsible for designing, developing, and deploying machine learning models. They work with data scientists and other engineers to ensure that models are accurate and efficient. This course will help you build a strong foundation in MLOps, which is essential for success as a Machine Learning Engineer. You will learn how to manage the MLOps lifecycle, from infrastructure setup to model deployment. You will also learn how to implement best practices for model development and deployment, which will help you to ensure that your models are reliable and performant.
See salaries and explore the career path for Machine Learning Engineer
Data Scientist
Data Scientists use machine learning and other statistical techniques to extract insights from data. They work with businesses to identify opportunities and solve problems. This course will help you build a foundation in MLOps, which is essential for success as a Data Scientist. You will learn how to manage the MLOps lifecycle, from infrastructure setup to model deployment. You will also learn how to implement best practices for model development and deployment, which will help you to ensure that your models are accurate and reliable.
See salaries and explore the career path for Data Scientist
Software Engineer
Software Engineers design, develop, and maintain software systems. They work with other engineers and stakeholders to ensure that software systems meet the needs of users. This course may be useful for Software Engineers who want to learn more about MLOps. You will learn how to manage the MLOps lifecycle, from infrastructure setup to model deployment. You will also learn how to implement best practices for model development and deployment, which will help you to ensure that your software systems are reliable and performant.
See salaries and explore the career path for Software Engineer
DevOps Engineer
DevOps Engineers work with development and operations teams to ensure that software systems are deployed and maintained efficiently. They use automation and other tools to streamline the software development process. This course may be useful for DevOps Engineers who want to learn more about MLOps. You will learn how to manage the MLOps lifecycle, from infrastructure setup to model deployment. You will also learn how to implement best practices for model development and deployment, which will help you to ensure that your software systems are reliable and performant.
See salaries and explore the career path for DevOps Engineer
Cloud Architect
Cloud Architects design and manage cloud computing systems. They work with businesses to identify opportunities to use cloud computing to improve efficiency and reduce costs. This course may be useful for Cloud Architects who want to learn more about MLOps. You will learn how to manage the MLOps lifecycle, from infrastructure setup to model deployment. You will also learn how to implement best practices for model development and deployment, which will help you to ensure that your cloud computing systems are reliable and performant.
See salaries and explore the career path for Cloud Architect
Data Analyst
Data Analysts use data to identify trends and patterns. They work with businesses to improve decision-making and solve problems. This course may be useful for Data Analysts who want to learn more about MLOps. You will learn how to manage the MLOps lifecycle, from infrastructure setup to model deployment. You will also learn how to implement best practices for model development and deployment, which will help you to ensure that your data analysis is accurate and reliable.
See salaries and explore the career path for Data Analyst
Business Analyst
Business Analysts work with businesses to identify opportunities and solve problems. They use data analysis and other techniques to help businesses make better decisions. This course may be useful for Business Analysts who want to learn more about MLOps. You will learn how to manage the MLOps lifecycle, from infrastructure setup to model deployment. You will also learn how to implement best practices for model development and deployment, which will help you to ensure that your business analysis is accurate and reliable.
See salaries and explore the career path for Business Analyst
Project Manager
Project Managers plan and execute projects. They work with teams to ensure that projects are completed on time, within budget, and to the required quality. This course may be useful for Project Managers who want to learn more about MLOps. You will learn how to manage the MLOps lifecycle, from infrastructure setup to model deployment. You will also learn how to implement best practices for model development and deployment, which will help you to ensure that your projects are successful.
See salaries and explore the career path for Project Manager
Product Manager
Product Managers work with teams to develop and launch new products. They are responsible for defining the product vision, roadmap, and features. This course may be useful for Product Managers who want to learn more about MLOps. You will learn how to manage the MLOps lifecycle, from infrastructure setup to model deployment. You will also learn how to implement best practices for model development and deployment, which will help you to ensure that your products are successful.
See salaries and explore the career path for Product Manager
Technical Writer
Technical Writers create documentation for software and other technical products. They work with engineers and other stakeholders to ensure that documentation is accurate and easy to understand. This course may be useful for Technical Writers who want to learn more about MLOps. You will learn how to manage the MLOps lifecycle, from infrastructure setup to model deployment. You will also learn how to implement best practices for model development and deployment, which will help you to ensure that your documentation is accurate and reliable.
See salaries and explore the career path for Technical Writer
Quality Assurance Analyst
Quality Assurance Analysts test software and other products to ensure that they meet quality standards. They work with development teams to identify and fix defects. This course may be useful for Quality Assurance Analysts who want to learn more about MLOps. You will learn how to manage the MLOps lifecycle, from infrastructure setup to model deployment. You will also learn how to implement best practices for model development and deployment, which will help you to ensure that your software products are reliable and performant.
See salaries and explore the career path for Quality Assurance Analyst
IT Manager
IT Managers are responsible for planning, implementing, and managing IT systems. They work with businesses to identify opportunities to use technology to improve efficiency and reduce costs. This course may be useful for IT Managers who want to learn more about MLOps. You will learn how to manage the MLOps lifecycle, from infrastructure setup to model deployment. You will also learn how to implement best practices for model development and deployment, which will help you to ensure that your IT systems are reliable and performant.
See salaries and explore the career path for IT Manager
Data Engineer
Data Engineers build and maintain data pipelines. They work with data scientists and other engineers to ensure that data is available and usable for analysis. This course may be useful for Data Engineers who want to learn more about MLOps. You will learn how to manage the MLOps lifecycle, from infrastructure setup to model deployment. You will also learn how to implement best practices for model development and deployment, which will help you to ensure that your data pipelines are reliable and performant.
See salaries and explore the career path for Data Engineer
Database Administrator
Database Administrators are responsible for managing and maintaining databases. They work with businesses to ensure that databases are reliable and performant. This course may be useful for Database Administrators who want to learn more about MLOps. You will learn how to manage the MLOps lifecycle, from infrastructure setup to model deployment. You will also learn how to implement best practices for model development and deployment, which will help you to ensure that your databases are reliable and performant.
See salaries and explore the career path for Database Administrator
Systems Administrator
Systems Administrators are responsible for managing and maintaining computer systems. They work with businesses to ensure that computer systems are reliable and performant. This course may be useful for Systems Administrators who want to learn more about MLOps. You will learn how to manage the MLOps lifecycle, from infrastructure setup to model deployment. You will also learn how to implement best practices for model development and deployment, which will help you to ensure that your computer systems are reliable and performant.
See salaries and explore the career path for Systems Administrator

Reading list

We've selected eight books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Demystifying Machine Learning Operations (MLOps).
Cover image
Mastering Machine Learning Algorithms
Save
Provides a comprehensive overview of machine learning systems, covering topics such as model selection, training, and evaluation. It valuable resource for learners looking to gain a deeper understanding of the theoretical foundations of machine learning.
Mastering Machine Learning Algorithms - Second...
Paperback
$$
Mastering Machine Learning Algorithms: Expert...
Paperback
$$
Mastering Machine Learning Algorithms: Expert...
Kindle Edition
$$
Mastering Machine Learning Algorithms: Expert...
Kindle Edition
$$
Cover image
Designing Data-Intensive Applications
Save
Provides valuable background knowledge on designing and building data-intensive applications. It covers topics such as data modeling, storage, and processing, which are essential for understanding the infrastructure aspects of MLOps.
Designing Data-Intensive Applications: The Big...
Paperback
$$
Designing Data-Intensive Applications: The Big...
Kindle Edition
$$
Cover image
Deep Learning
Save
Provides a comprehensive overview of deep learning concepts and algorithms. It valuable resource for learners looking to gain a strong foundation in deep learning, which is an essential component of many modern machine learning models.
Deep Learning (Adaptive Computation and Machine...
Hardcover
$$$
Deep Learning (Adaptive Computation and Machine...
Kindle Edition
$$$
Pattern Recognition and Machine Learning
Save
Provides a comprehensive overview of machine learning techniques, including supervised and unsupervised learning. It valuable reference for learners looking to gain a broader understanding of the theoretical foundations of machine learning.
Pattern Recognition and Machine Learning...
Hardcover
$$$
Pattern Recognition and Machine Learning...
Paperback
$$$
Pattern Recognition and Machine Learning (text...
Unknown Binding
$$$$
Cover image
Introduction to Machine Learning with Python
Save
Provides a hands-on introduction to machine learning using Python. It valuable resource for learners who are new to machine learning and looking to gain practical experience in applying machine learning techniques to real-world problems.
Introduction to Machine Learning with Python: A...
Kindle Edition
$$
Cover image
Introduction to Artificial Intelligence
Save
Provides a comprehensive overview of artificial intelligence concepts and techniques. It valuable resource for learners looking to gain a broader understanding of the theoretical foundations of artificial intelligence, which is the broader field that encompasses machine learning.
Introduction to Artificial Intelligence
Paperback
Check
Introduction to Artificial Intelligence
Kindle Edition
Check
Statistical Learning with Sparsity
Save
Provides a comprehensive overview of sparse modeling techniques, which are commonly used in machine learning for feature selection and model regularization. It valuable reference for learners looking to gain a deeper understanding of these techniques.
Statistical Learning with Sparsity
Paperback
Check
Statistical Learning with Sparsity
Kindle Edition
Check
Cover image
Deep Learning for Coders with fastai and PyTorch
Save
Provides a practical introduction to deep learning using the fastai library. It valuable resource for learners who are new to deep learning and looking for a hands-on approach using Python.
Deep Learning for Coders with Fastai and PyTorch:...
Paperback
$$
Deep Learning for Coders with fastai and PyTorch:...
Kindle Edition
$$

Share

Help others find this course page by sharing it with your friends and followers:
Facebook
LinkedIn
Reddit
X
Link
Email

Similar courses

Here are four courses similar to Demystifying Machine Learning Operations (MLOps).
Developing Machine Learning Solutions
Most relevant
Machine Learning Operations with Vertex AI: Model...
Most relevant
Machine Learning Operations (MLOps) with Vertex AI:...
Most relevant
Designing Machine Learning Solutions on Microsoft Azure
Most relevant

Pluralsight Buyer Protection & Guarantee

Verified partner policies & purchase protections

Enterprise Technology Platform
Free Trial & Audit

10-Day Free Trial

10-day free trial (up to 200 minutes of content) with access to the Skills video library and skill assessments. Cancel before day 10 to avoid automatic conversion.

Return & Refund Policy

Cancel Anytime (Non-Refundable Once Billed)

Cancel easily online anytime before the 10-day trial expires to prevent billing. Once subscription fees are paid, individual subscriptions are non-refundable (cancellation stops future renewals).

Buyer Protections

Verified Academic & Industry Partner

Skill IQ and Role IQ diagnostic benchmark assessments, hands-on lab environments, and curated learning paths designed by technology leaders.

Key Purchase Protections
10-day free trial on individual Skills plans
Skill IQ assessments to benchmark proficiency
Cancel anytime online before trial ends without fees
Hands-on interactive labs and enterprise-grade tech content
OpenCourser Verified Partner Policy details last reviewed: 2026-10-22
Course image
Time
134 hours
Level
Intermediate
Via
Pluralsight
Instructor
Mohammed Osman
Language
English
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Teaches best practices for machine learning development and deployment
Covers tools and methods that align with industry standards
Helps learners manage and monitor machine learning models throughout the lifecycle
Share this
Share to help others discover this course.
Facebook LinkedIn X Reddit Link Email
Begin learning today
This course is currently discontinued or unavailable. See active alternatives above.
Course Unavailable
Save for later
Add this course to your list. Find it anytime.
Save
Our mission

OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.

Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.

Find this site helpful? Tell a friend about us.

Affiliate disclosure

We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.

Your purchases help us maintain our catalog and keep our servers humming without ads.

Thank you for supporting OpenCourser.

© 2016 - 2025 OpenCourser