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MLOps (Machine Learning Operations) Fundamentals

Google Cloud

This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud.

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This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud.

This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Machine Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. They work with (or can be) Data Scientists, who develop models, to enable velocity and rigor in deploying the best performing models.

This course is no longer available. Find something similar by browsing:
MLOps Machine Learning Google Cloud Kubernetes AI Platform Pipelines Kubeflow Pipelines CI/CD

What's inside

Syllabus

Welcome to MLOps Fundamentals
Why and When do we Need MLOps
Understanding the Main Kubernetes Components (Optional)
Introduction to AI Platform Pipelines
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Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Focuses on the deployment, evaluation, monitoring, operations, and automation of ML systems in production, which is currently used in the industry
Emphasizes the use of tools for continuous analysis and evaluation of deployed models, which is a key component of MLOps
Covers Kubernetes and Kubeflow Pipelines, which are widely adopted platforms for deploying and managing ML systems
Taught by Google Cloud, which provides the tools and platforms used in the course and has real-world experience in MLOps
Assumes prior knowledge of ML and cloud computing, which may not be suitable for complete beginners
May require participants to have access to Google Cloud Platform for hands-on practice, which can involve costs

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Reviews summary

Mlops foundation with gcp & kubeflow

According to students, this course offers a solid introduction to MLOps on Google Cloud, focusing on practical implementation using AI Platform Pipelines and Kubeflow. Learners highlight the hands-on labs and clear explanations as particularly beneficial for building a foundational understanding. While many find it excellent for beginners and those new to MLOps, some experienced professionals suggest it can be superficial for deeper dives, requiring additional resources. A recurring point is the recommendation for the optional Kubernetes module to be mandatory due to its foundational importance. Overall, it's considered a valuable starting point for production ML systems.
Benefits from prior understanding of ML and basic cloud concepts.
"It does require some prior understanding of ML and basic cloud concepts to fully benefit."
"I had some prior experience with ML but not MLOps, and this filled the gap perfectly."
Emphasizes hands-on experience with tools like Kubeflow Pipelines.
"...the labs were very practical. I particularly appreciated the focus on Kubeflow Pipelines and CI/CD..."
"Excellent course for getting hands-on with MLOps. The integration with AI Platform and Kubeflow was seamless..."
"The hands-on exercises were helpful, though I wished for more complex scenarios."
Provides a strong introduction to MLOps concepts and tools on Google Cloud.
"This course is a fantastic introduction to MLOps on Google Cloud. The concepts were explained clearly..."
"Overall, a good foundational course. It covers the basics of MLOps on GCP effectively."
"This course delivered exactly what it promised: a fundamental understanding of MLOps on GCP."
The optional Kubernetes section is vital for comprehensive understanding.
"Some parts felt a bit rushed, especially around the deeper dive into Kubernetes, which was marked optional but felt necessary..."
"I think the 'optional' Kubernetes section should be mandatory, as it's crucial for understanding the underlying infrastructure."
"The optional Kubernetes module is a must-do."
May be too superficial for experienced ML engineers seeking deep dives.
"I was hoping for a more in-depth course. While it covers the basics, I felt it didn't provide enough detail..."
"The course provides a decent overview, but I found some of the content a bit superficial."
"For someone completely new, it's a good start, but for those with some background, it might lack depth."

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 MLOps (Machine Learning Operations) Fundamentals with these activities:
Review Concepts in Model Deployment and Monitoring
Refreshing knowledge in model deployment and monitoring will strengthen the foundation for understanding MLOps concepts
Browse courses on Model Deployment
Show steps
  • Review course materials and lecture notes
  • Revisit previous projects and assignments
  • Complete practice quizzes and exercises
Gather Resources on Best Practices for MLOps
Gathering resources on best practices for MLOps will provide a comprehensive understanding of industry standards and best practices
Show steps
  • Identify relevant resources from Google Cloud documentation
  • Review whitepapers and articles from industry experts
  • Explore case studies and success stories
  • Organize and document the findings
Discuss Challenges in Deploying ML Models
Discussing challenges in deploying ML models with peers will foster collaboration, exchange of ideas, and provide diverse perspectives
Browse courses on Model Deployment
Show steps
  • Identify common challenges faced in deploying ML models
  • Share experiences and lessons learned
  • Brainstorm solutions and strategies for addressing the challenges
Four other activities
Expand to see all activities and additional details
Show all seven activities
Deploy a Sample ML Model
Deploying a sample ML model will give practical hands-on experience in deploying models on Google Cloud
Browse courses on Model Deployment
Show steps
  • Choose a pre-trained model from the TensorFlow Hub
  • Create an AI Platform Endpoint service
  • Deploy the model to the endpoint
  • Test the deployed model
Practice Deploying Models with Different Frameworks
Practicing deploying models with different frameworks will help solidify the understanding of deploying models and the different frameworks available
Browse courses on Model Deployment
Show steps
  • Deploy a model trained in TensorFlow
  • Deploy a model trained in PyTorch
  • Deploy a model trained in Keras
  • Compare the deployment process and performance of the models
Follow Tutorials on Advanced MLOps Techniques
Following tutorials on advanced MLOps techniques will expand knowledge and skills in deploying and managing ML systems
Browse courses on Model Deployment
Show steps
  • Identify tutorials on specific MLOps techniques
  • Follow the tutorials and implement the techniques
  • Evaluate the effectiveness of the techniques
Create a Model Monitoring Dashboard
Creating a model monitoring dashboard will provide hands-on experience in monitoring models in production and identifying potential issues
Browse courses on Model Monitoring
Show steps
  • Create a monitoring pipeline using AI Platform Pipelines
  • Define metrics for monitoring model performance
  • Set up alerts and notifications
  • Visualize the monitoring data in a dashboard

Career center

Learners who complete MLOps (Machine Learning Operations) Fundamentals will develop knowledge and skills that may be useful to these careers:
Cloud ML Engineer
Cloud ML Engineers are responsible for designing, developing, and managing ML systems in the cloud. This course introduces participants to tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. By taking this course, Cloud ML Engineers may gain valuable insights and skills that can help them advance in their careers.
See salaries and explore the career path for Cloud ML Engineer
Machine Learning Engineer
Machine Learning Engineers work with Data Scientists to build and deploy ML models. This course introduces participants to tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. With this course's help, Machine Learning Engineers may be better equipped to handle a wider range of tasks and responsibilities in their roles.
See salaries and explore the career path for Machine Learning Engineer
AI Engineer
AI Engineers design, build, and manage AI systems. This course introduces participants to tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. AI Engineers may take this course to learn about the latest tools and best practices for ML systems.
See salaries and explore the career path for AI Engineer
ML Architect
ML Architects design, build, and manage ML systems. This course introduces participants to tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. ML Architects may take this course to learn about the latest tools and best practices for ML systems.
See salaries and explore the career path for ML Architect
Data Science Manager
Data Science Managers lead teams of data scientists and oversee the development and implementation of ML solutions. This course introduces participants to tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. Data Science Managers may take this course to learn about the latest tools and best practices for ML systems.
See salaries and explore the career path for Data Science Manager
ML Researcher
ML Researchers develop new ML algorithms and techniques. This course introduces participants to tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. ML Researchers may take this course to learn about the latest tools and best practices for ML systems.
See salaries and explore the career path for ML Researcher
Data Scientist
Data Scientists develop and refine models using complex ML algorithms. They feed data into models and test them for accuracy. This course introduces participants to tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. The skills and knowledge developed in this course may be helpful to Data Scientists who wish to advance into more senior roles.
See salaries and explore the career path for Data Scientist
Cloud Architect
Cloud Architects design, build, and manage cloud-based systems. This course introduces participants to tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. Cloud Architects may take this course to learn about ML systems and how to integrate them into their cloud-based systems.
See salaries and explore the career path for Cloud Architect
DevOps Engineer
DevOps Engineers work closely with software developers to ensure that software is built, tested, and deployed efficiently. This course introduces participants to tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. By taking this course, DevOps Engineers may learn about ML systems and how to integrate them into their work.
See salaries and explore the career path for DevOps Engineer
Data Engineer
Data Engineers design, build, and manage data pipelines. This course introduces participants to tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. Data Engineers may take this course to learn about ML systems and how to integrate them into their data pipelines.
See salaries and explore the career path for Data Engineer
Software Engineer
Software Engineers design, develop, and test software systems. This course introduces participants to tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. Software Engineers may take this course to learn about ML systems and how to incorporate them into their software.
See salaries and explore the career path for Software Engineer
Business Analyst
Business Analysts help organizations understand their business needs and develop solutions to meet those needs. This course introduces participants to tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. Business Analysts may take this course to learn about ML systems and how they can be used to improve business processes.
See salaries and explore the career path for Business Analyst
IT Manager
IT Managers are responsible for the planning, implementation, and management of an organization's IT systems. This course introduces participants to tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. IT Managers may take this course to learn about ML systems and how they can be used to improve their IT systems.
See salaries and explore the career path for IT Manager
Data Analyst
Data Analysts collect, clean, and analyze data. This course introduces participants to tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. Data Analysts may take this course to learn about ML systems and how to use them to improve their data analysis.
See salaries and explore the career path for Data Analyst
Product Manager
Product Managers are responsible for the development and launch of new products. This course introduces participants to tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. Product Managers may take this course to learn about ML systems and how they can be used to develop new products.
See salaries and explore the career path for Product Manager

Reading list

We've selected five 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 MLOps (Machine Learning Operations) Fundamentals.
Cover image
Kubernetes in Action, Second Edition
Save
Provides a comprehensive overview of Kubernetes, including its architecture, components, and features. It valuable resource for anyone looking to learn more about Kubernetes or use it to deploy ML systems.
Kubernetes in Action, Second Edition
Paperback
$$$
Cover image
Hands-On Machine Learning with Scikit-Learn, Keras,...
Save
Provides a comprehensive overview of machine learning with Scikit-Learn, Keras, and TensorFlow, including its concepts, algorithms, and applications. It valuable resource for anyone looking to learn more about machine learning or use it to build ML systems.
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Kindle Edition
$$
Machine Learning Con Pytorch Y Scikit Lean
Save
Provides a comprehensive overview of machine learning with Python, including its concepts, algorithms, and applications. It valuable resource for anyone looking to learn more about machine learning or use it to build ML systems.
(Español) Machine Learning con PyTorch y Scikit-Learn:...
Paperback
$$$
Cover image
Machine Learning with R
Save
Provides a comprehensive overview of machine learning with R, including its concepts, algorithms, and applications. It valuable resource for anyone looking to learn more about machine learning or use it to build ML systems.
Machine Learning with R - Fourth Edition: Learn...
Paperback
$$
Machine Learning with R - Second Edition: Expert...
Kindle Edition
$$
Machine Learning with R - Third Edition: Expert...
Paperback
$$
(Italiano) Machine Learning con R: Conoscere le tecniche per...
Kindle Edition
$$
R: Unleash Machine Learning Techniques
Kindle Edition
$$$
Machine Learning with R: Learn How to Use R to...
Paperback
$$
(中文) Machine learning and R language (3rd edition of the...
Paperback
$$
Cover image
TensorFlow Deep Learning Projects
Save
Provides a comprehensive guide to building and managing machine learning pipelines, covering both technical and organizational aspects.
TensorFlow Deep Learning Projects: 10 real-world...
Kindle Edition
$$

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Course image
Time
332 hours
Level
Introductory
Via
Pluralsight
Instructor
Google Cloud
Language
English
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Focuses on the deployment, evaluation, monitoring, operations, and automation of ML systems in production, which is currently used in the industry
Emphasizes the use of tools for continuous analysis and evaluation of deployed models, which is a key component of MLOps
Covers Kubernetes and Kubeflow Pipelines, which are widely adopted platforms for deploying and managing ML systems
Taught by Google Cloud, which provides the tools and platforms used in the course and has real-world experience in MLOps
Assumes prior knowledge of ML and cloud computing, which may not be suitable for complete beginners
May require participants to have access to Google Cloud Platform for hands-on practice, which can involve costs
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