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Paweł Kordek
Efficiently delivering machine learning products is not easy, therefore good tools that support ML model development are needed. This course will teach you MLflow.
Read more
Efficiently delivering machine learning products is not easy, therefore good tools that support ML model development are needed. This course will teach you MLflow.
Read more
Efficiently delivering machine learning products is not easy, therefore good tools that support ML model development are needed. This course will teach you MLflow.
Developing machine learning models in teams, with real-world data and serving real-world business needs may be complex. In this course, Getting Started with MLflow, you’ll learn to manage the full lifecycle of machine learning models. First, you’ll explore how to track your machine learning experiments for easy comparison and reproducibility. Next, you’ll discover ways of using MLflow to collaborate on model development in teams of any size. Finally, you’ll learn how to share your models in a way that makes them ready for use in real products. When you’re finished with this course, you’ll have the skills and knowledge of MLflow needed to create machine learning models in a collaborative, reproducible, and production-ready way.
Register for this course and see more details by visiting:
OpenCourser.com/course/l004op/getting
What's inside
Syllabus
Course Overview
Understanding MLflow
Tracking ML Experiments
Exporting Artifacts
Read more
Syllabus
Course Overview
Understanding MLflow
Tracking ML Experiments
Exporting Artifacts
Read more
Using MLflow in a Collaborative Scenario
Packaging and Running Models
Sharing and Managing Models with Model Registry
Summary
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Understanding MLflow is pivotal in the efficient delivery of machine learning products
This training comprehensively covers the machine learning model lifecycle
Teaches how to collaborate on model development seamlessly in teams
Prepares models for real-world applications through packaging and running
Facilitates knowledge of MLflow's Model Registry, ensuring proper model management
Taught by industry experts in the field
Save this course
Save Getting Started with MLflow 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 Getting Started with MLflow with these
activities:
Review ML concepts
Show steps
Refresh your understanding of core ML concepts to ensure a solid foundation for the course.
Show steps
-
Review supervised learning algorithms (e.g., linear regression, decision trees).
-
Go over unsupervised learning techniques (e.g., clustering, dimensionality reduction).
-
Recall ML evaluation metrics (e.g., accuracy, ROC AUC).
Attend MLflow workshops
Show steps
Expand your knowledge of MLflow and connect with experts by attending workshops focused on MLflow.
Show steps
-
Research and identify relevant MLflow workshops.
-
Register and attend the workshops.
-
Actively participate in discussions and hands-on exercises.
Attend an MLflow Workshop
Show steps
Attending an MLflow workshop will allow you to learn from MLflow experts and get hands-on experience with the platform.
Show steps
-
Research MLflow workshops in your area.
-
Register for a workshop.
-
Attend the workshop and participate actively.
One other activity
Expand to see all activities and additional details
Show all four activities
Develop an ML workflow using MLflow
Show steps
Enhance your understanding of MLflow by creating a comprehensive ML workflow that encompasses experiment tracking, model training, and deployment.
Show steps
-
Design an ML workflow that meets specific business requirements.
-
Implement the workflow using MLflow, integrating experiment tracking, model training, and deployment.
-
Evaluate and iterate on the workflow to optimize performance and efficiency.
Review ML concepts
Show steps
Refresh your understanding of core ML concepts to ensure a solid foundation for the course.
Show steps
Show steps
Show steps
- Review supervised learning algorithms (e.g., linear regression, decision trees).
- Go over unsupervised learning techniques (e.g., clustering, dimensionality reduction).
- Recall ML evaluation metrics (e.g., accuracy, ROC AUC).
Attend MLflow workshops
Show steps
Expand your knowledge of MLflow and connect with experts by attending workshops focused on MLflow.
Show steps
Show steps
Show steps
- Research and identify relevant MLflow workshops.
- Register and attend the workshops.
- Actively participate in discussions and hands-on exercises.
Attend an MLflow Workshop
Show steps
Attending an MLflow workshop will allow you to learn from MLflow experts and get hands-on experience with the platform.
Show steps
Show steps
Show steps
- Research MLflow workshops in your area.
- Register for a workshop.
- Attend the workshop and participate actively.
One other activity
Expand to see all activities and additional details
Show all four activities
Develop an ML workflow using MLflow
Show steps
Enhance your understanding of MLflow by creating a comprehensive ML workflow that encompasses experiment tracking, model training, and deployment.
Show steps
Show steps
Show steps
- Design an ML workflow that meets specific business requirements.
- Implement the workflow using MLflow, integrating experiment tracking, model training, and deployment.
- Evaluate and iterate on the workflow to optimize performance and efficiency.
Career center
Learners who complete Getting Started with MLflow will develop knowledge and skills
that may be useful to these careers:
Machine Learning Researcher
Machine Learning Researcher
A Machine Learning Researcher is responsible for the development of new machine learning algorithms and techniques. This course may be useful in supporting this role by providing a foundation in the use of MLflow, a tool for managing the lifecycle of machine learning models. The course covers topics such as tracking experiments, exporting artifacts, collaborating on model development, and packaging and running models. These skills can help Machine Learning Researchers to create and manage machine learning models in a more efficient and collaborative way.
Data Scientist
Data Scientist
A Data Scientist is responsible for the collection, analysis, and interpretation of data. This course may be useful in supporting this role by providing a foundation in the use of MLflow, a tool for managing the lifecycle of machine learning models. The course covers topics such as tracking experiments, exporting artifacts, collaborating on model development, and packaging and running models. These skills can help Data Scientists to create and manage machine learning models in a more efficient and collaborative way.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer is responsible for the design, development, and deployment of machine learning models. This course may be useful in supporting this role by providing a foundation in the use of MLflow, a tool for managing the lifecycle of machine learning models. The course covers topics such as tracking experiments, exporting artifacts, collaborating on model development, and packaging and running models. These skills can help Machine Learning Engineers to create and manage machine learning models in a more efficient and collaborative way.
Applied Scientist
Applied Scientist
An Applied Scientist is responsible for the application of scientific principles to the development of new products and services. This course may be useful in supporting this role by providing a foundation in the use of MLflow, a tool for managing the lifecycle of machine learning models. The course covers topics such as tracking experiments, exporting artifacts, collaborating on model development, and packaging and running models. These skills can help Applied Scientists to create and manage machine learning models in a more efficient and collaborative way.
Software Engineer
Software Engineer
A Software Engineer is responsible for the design, development, and maintenance of software systems. This course may be useful in supporting this role by providing a foundation in the use of MLflow, a tool for managing the lifecycle of machine learning models. The course covers topics such as tracking experiments, exporting artifacts, collaborating on model development, and packaging and running models. These skills can help Software Engineers to create and manage machine learning models in a more efficient and collaborative way.
Data Analyst
Data Analyst
A Data Analyst is responsible for the collection, analysis, and interpretation of data. This course may be useful in supporting this role by providing a foundation in the use of MLflow, a tool for managing the lifecycle of machine learning models. The course covers topics such as tracking experiments, exporting artifacts, collaborating on model development, and packaging and running models. These skills can help Data Analysts to create and manage machine learning models in a more efficient and collaborative way.
Data Engineer
Data Engineer
A Data Engineer is responsible for the design, development, and maintenance of data systems. This course may be useful in supporting this role by providing a foundation in the use of MLflow, a tool for managing the lifecycle of machine learning models. The course covers topics such as tracking experiments, exporting artifacts, collaborating on model development, and packaging and running models. These skills can help Data Engineers to create and manage machine learning models in a more efficient and collaborative way.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst is responsible for the development and use of mathematical and statistical models to analyze financial data. This course may be useful in supporting this role by providing a foundation in the use of MLflow, a tool for managing the lifecycle of machine learning models. The course covers topics such as tracking experiments, exporting artifacts, collaborating on model development, and packaging and running models. These skills can help Quantitative Analysts to create and manage machine learning models in a more efficient and collaborative way.
Research Scientist
Research Scientist
A Research Scientist is responsible for the conduct of scientific research and the development of new knowledge. This course may be useful in supporting this role by providing a foundation in the use of MLflow, a tool for managing the lifecycle of machine learning models. The course covers topics such as tracking experiments, exporting artifacts, collaborating on model development, and packaging and running models. These skills can help Research Scientists to create and manage machine learning models in a more efficient and collaborative way.
Business Analyst
Business Analyst
A Business Analyst is responsible for the analysis of business processes and the development of solutions to improve efficiency. This course may be useful in supporting this role by providing a foundation in the use of MLflow, a tool for managing the lifecycle of machine learning models. The course covers topics such as tracking experiments, exporting artifacts, collaborating on model development, and packaging and running models. These skills can help Business Analysts to understand the technical aspects of machine learning models and to make informed decisions about their use in business processes.
DevOps Engineer
DevOps Engineer
A DevOps Engineer is responsible for the development and maintenance of software systems. This course may be useful in supporting this role by providing a foundation in the use of MLflow, a tool for managing the lifecycle of machine learning models. The course covers topics such as tracking experiments, exporting artifacts, collaborating on model development, and packaging and running models. These skills can help DevOps Engineers to create and manage machine learning models in a more efficient and collaborative way.
Project Manager
Project Manager
A Project Manager is responsible for the management of projects. This course may be useful in supporting this role by providing a foundation in the use of MLflow, a tool for managing the lifecycle of machine learning models. The course covers topics such as tracking experiments, exporting artifacts, collaborating on model development, and packaging and running models. These skills can help Project Managers to understand the technical aspects of machine learning models and to make informed decisions about their development and deployment.
Product Manager
Product Manager
A Product Manager is responsible for the development and management of products. This course may be useful in supporting this role by providing a foundation in the use of MLflow, a tool for managing the lifecycle of machine learning models. The course covers topics such as tracking experiments, exporting artifacts, collaborating on model development, and packaging and running models. These skills can help Product Managers to understand the technical aspects of machine learning models and to make informed decisions about their development and deployment.
Technical Program Manager
Technical Program Manager
A Technical Program Manager is responsible for the management of technical programs and projects. This course may be useful in supporting this role by providing a foundation in the use of MLflow, a tool for managing the lifecycle of machine learning models. The course covers topics such as tracking experiments, exporting artifacts, collaborating on model development, and packaging and running models. These skills can help Technical Program Managers to understand the technical aspects of machine learning models and to make informed decisions about their development and deployment.
Cloud Architect
Cloud Architect
A Cloud Architect is responsible for the design and implementation of cloud computing solutions. This course may be useful in supporting this role by providing a foundation in the use of MLflow, a tool for managing the lifecycle of machine learning models. The course covers topics such as tracking experiments, exporting artifacts, collaborating on model development, and packaging and running models. These skills can help Cloud Architects to create and manage machine learning models in a more efficient and collaborative way.
For more career information including salaries, visit:
OpenCourser.com/course/l004op/getting
Reading list
We've selected six 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
Getting Started with MLflow.
Provides a general overview of machine learning concepts using Python. It provides good background information for this course.
Provides a thorough review of machine learning algorithms. A good reference when wanting to learn more about the algorithms that make up the field of machine learning.
Provides more general knowledge on machine learning concepts and applications. It can be read as a way to complement the information provided by the course.
Provides a general overview of machine learning. It can be a good starting point to build background knowledge for this course.
Can be used to supplement the course in providing more background information on machine learning. A good introduction to the field.
Provides a broad overview of machine learning concepts and techniques. Could be valuable to read before taking the course for background information.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/l004op/getting
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Time
82 hours
Level
Introductory
Via
Pluralsight
Instructor
Paweł Kordek
Language
English
Good to know
Understanding MLflow is pivotal in the efficient delivery of machine learning products
This training comprehensively covers the machine learning model lifecycle
Teaches how to collaborate on model development seamlessly in teams
Prepares models for real-world applications through packaging and running
Facilitates knowledge of MLflow's Model Registry, ensuring proper model management
Taught by industry experts in the field
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