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Machine Learning Operations (MLOps)
Model Training
Model Packaging
Model Validation
Model Deployment
Model Monitoring
DevOps
Register for this course and see more details by visiting:
OpenCourser.com/course/iq5w5h/manage
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Designed for data engineers, data scientists, and AppDev professionals
Provides a practical approach to optimizing model training, packaging, validation, deployment, and monitoring
Emphasizes automation and auditing within existing DevOps tooling
Focuses on storing and versioning models in production
Covers collecting continuous feedback on model behavior
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Manage Your End-to-end Machine Learning Lifecycle with MLOps.
These are activities you can do either before, during, or after a course.
Career center
Learners who complete Manage Your End-to-end Machine Learning Lifecycle with MLOps will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use their expertise in machine learning, statistics, and programming to build and deploy models that can solve business problems. This course can help Data Scientists build a foundation in MLOps, which is a set of best practices for developing, deploying, and monitoring machine learning models in production. By learning how to use MLOps tools and techniques, Data Scientists can improve the quality and efficiency of their work.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for building, deploying, and maintaining machine learning models. This course can help Machine Learning Engineers learn about the MLOps lifecycle, which is a set of best practices for developing, deploying, and monitoring machine learning models in production. By learning how to use MLOps tools and techniques, Machine Learning Engineers can improve the quality and efficiency of their work.
Data Engineer
Data Engineer
Data Engineers are responsible for building and maintaining the data infrastructure that supports machine learning models. This course can help Data Engineers learn about the MLOps lifecycle, which is a set of best practices for developing, deploying, and monitoring machine learning models in production. By learning how to use MLOps tools and techniques, Data Engineers can improve the quality and efficiency of their work.
Software Engineer
Software Engineer
Software Engineers are responsible for designing, developing, and maintaining software applications. This course can help Software Engineers learn about the MLOps lifecycle, which is a set of best practices for developing, deploying, and monitoring machine learning models in production. By learning how to use MLOps tools and techniques, Software Engineers can improve the quality and efficiency of their work.
DevOps Engineer
DevOps Engineer
DevOps Engineers are responsible for bridging the gap between development and operations teams. This course can help DevOps Engineers learn about the MLOps lifecycle, which is a set of best practices for developing, deploying, and monitoring machine learning models in production. By learning how to use MLOps tools and techniques, DevOps Engineers can improve the quality and efficiency of their work.
Cloud Architect
Cloud Architect
Cloud Architects are responsible for designing and managing cloud computing solutions. This course can help Cloud Architects learn about the MLOps lifecycle, which is a set of best practices for developing, deploying, and monitoring machine learning models in production. By learning how to use MLOps tools and techniques, Cloud Architects can improve the quality and efficiency of their work.
Data Analyst
Data Analyst
Data Analysts are responsible for collecting, cleaning, and analyzing data. This course can help Data Analysts learn about the MLOps lifecycle, which is a set of best practices for developing, deploying, and monitoring machine learning models in production. By learning how to use MLOps tools and techniques, Data Analysts can improve the quality and efficiency of their work.
Business Analyst
Business Analyst
Business Analysts are responsible for understanding business needs and translating them into technical requirements. This course can help Business Analysts learn about the MLOps lifecycle, which is a set of best practices for developing, deploying, and monitoring machine learning models in production. By learning how to use MLOps tools and techniques, Business Analysts can improve the quality and efficiency of their work.
Product Manager
Product Manager
Product Managers are responsible for defining and managing the product roadmap. This course can help Product Managers learn about the MLOps lifecycle, which is a set of best practices for developing, deploying, and monitoring machine learning models in production. By learning how to use MLOps tools and techniques, Product Managers can improve the quality and efficiency of their work.
Project Manager
Project Manager
Project Managers are responsible for planning and executing projects. This course can help Project Managers learn about the MLOps lifecycle, which is a set of best practices for developing, deploying, and monitoring machine learning models in production. By learning how to use MLOps tools and techniques, Project Managers can improve the quality and efficiency of their work.
Technical Writer
Technical Writer
Technical Writers are responsible for creating documentation for software products. This course can help Technical Writers learn about the MLOps lifecycle, which is a set of best practices for developing, deploying, and monitoring machine learning models in production. By learning how to use MLOps tools and techniques, Technical Writers can improve the quality and efficiency of their work.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts are responsible for using mathematical and statistical models to solve business problems. This course can help Quantitative Analysts learn about the MLOps lifecycle, which is a set of best practices for developing, deploying, and monitoring machine learning models in production. By learning how to use MLOps tools and techniques, Quantitative Analysts can improve the quality and efficiency of their work.
Risk Analyst
Risk Analyst
Risk Analysts are responsible for identifying and assessing risks. This course can help Risk Analysts learn about the MLOps lifecycle, which is a set of best practices for developing, deploying, and monitoring machine learning models in production. By learning how to use MLOps tools and techniques, Risk Analysts can improve the quality and efficiency of their work.
Data Scientist Manager
Data Scientist Manager
Data Scientist Managers are responsible for leading and managing teams of Data Scientists. This course can help Data Scientist Managers learn about the MLOps lifecycle, which is a set of best practices for developing, deploying, and monitoring machine learning models in production. By learning how to use MLOps tools and techniques, Data Scientist Managers can improve the quality and efficiency of their work.
Machine Learning Manager
Machine Learning Manager
Machine Learning Managers are responsible for leading and managing teams of Machine Learning Engineers. This course can help Machine Learning Managers learn about the MLOps lifecycle, which is a set of best practices for developing, deploying, and monitoring machine learning models in production. By learning how to use MLOps tools and techniques, Machine Learning Managers can improve the quality and efficiency of their work.
For more career information including salaries, visit:
OpenCourser.com/course/iq5w5h/manage
Reading list
We've selected ten 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
Manage Your End-to-end Machine Learning Lifecycle with MLOps.
Provides a theoretical foundation for machine learning. This book would be a useful reference for someone who wants to delve more deeply into the theory.
Practical guide to deep learning with PyTorch. Students who are familiar with Python and want to expand their knowledge of deep learning may find this book useful.
Is more of a general introduction to the field of Machine Learning Engineering. It could be used to provide supplemental background material for the course.
Has a practical focus on machine learning algorithms and techniques. It could be useful for practitioners who want to get hands-on, but the course itself does not have any coding components.
Provides a visual introduction to deep learning concepts. This book may be useful for students who want to get hands-on, but the course itself does not have any coding components.
Comprehensive guide to machine learning in Python. The course does not have any coding components, but this book could be useful for practitioners who want to get hands-on.
Provides a practical introduction to machine learning with Python libraries. This book may be useful for practitioners who want to get hands-on, but the course itself does not have any coding components.
Gentle introduction to machine learning with Python. It can serve as a useful supplement for students who need a more foundational understanding of machine learning concepts.
Introduces fundamentals of deep learning in Python. The course does not have prerequisites, but this could prove useful for any students who might need to refresh their knowledge.
Focuses on some of the popular Python libraries for machine learning. This could be useful for practitioners who want to get hands-on, but the course itself does not have any coding components.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/iq5w5h/manage
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