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Google Cloud
This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud.
Read more
This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud.
Read more
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.
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OpenCourser.com/course/kgyg74/mlops
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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
Read more
Syllabus
Welcome to MLOps Fundamentals
Why and When do we Need MLOps
Understanding the Main Kubernetes Components (Optional)
Introduction to AI Platform Pipelines
Read more
Training, Tuning and Serving on AI Platform
Kubeflow Pipelines on AI Platform
CI/CD for Kubeflow Pipelines on AI Platform
Summary
Course Resources
Good to know
Good to know
Know what's good ,
what to watch for , 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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Save MLOps (Machine Learning Operations) Fundamentals to your list so you can find it easily later:
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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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Review Concepts in Model Deployment and Monitoring
Show steps
Refreshing knowledge in model deployment and monitoring will strengthen the foundation for understanding MLOps concepts
Browse courses on
Model Deployment
Show steps
Show steps
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
Show steps
Gathering resources on best practices for MLOps will provide a comprehensive understanding of industry standards and best practices
Show steps
Show steps
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
Show steps
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
Show steps
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
Show steps
Deploying a sample ML model will give practical hands-on experience in deploying models on Google Cloud
Browse courses on
Model Deployment
Show steps
Show steps
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
Show steps
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
Show steps
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
Show steps
Following tutorials on advanced MLOps techniques will expand knowledge and skills in deploying and managing ML systems
Browse courses on
Model Deployment
Show steps
Show steps
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
Show steps
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
Show steps
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 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.
Machine Learning 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.
AI 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.
Data Science Manager
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.
ML Architect
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.
ML Researcher
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.
Cloud Architect
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.
Data Scientist
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.
DevOps Engineer
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.
Data 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.
Software 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.
Data Analyst
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.
IT Manager
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.
Business Analyst
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.
Product Manager
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.
For more career information including salaries, visit:
OpenCourser.com/course/kgyg74/mlops
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.
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.
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.
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.
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.
Provides a comprehensive guide to building and managing machine learning pipelines, covering both technical and organizational aspects.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/kgyg74/mlops
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Time
332 hours
Level
Introductory
Via
Pluralsight
Instructor
Google Cloud
Language
English
Good to know
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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