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Jared Rhodes
In this course, you'll learn about how data science practitioners can utilize tools for managing the models they create. You'll also see those tools showcased in Microsoft Azure.
Read more
In this course, you'll learn about how data science practitioners can utilize tools for managing the models they create. You'll also see those tools showcased in Microsoft Azure.
Read more
In this course, you'll learn about how data science practitioners can utilize tools for managing the models they create. You'll also see those tools showcased in Microsoft Azure.
One of the most overlooked processes in data science is managing the life cycle of models. In this course, Deploying and Managing Models in Microsoft Azure, you'll gain foundational knowledge of Azure Machine Learning. First, you'll discover how to create and utilize Azure Machine Learning. Next, you'll find out how to integrate with Azure DevOps. Finally, you'll explore how to utilize them together to automate the deployment and management of models. When you're finished with this course, you'll have the skills and knowledge of model life cycle management needed to manage a machine learning project. Software required: Microsoft Azure.
Register for this course and see more details by visiting:
OpenCourser.com/course/azljev/deploying
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What's inside
Syllabus
Course Overview
Deploying a Machine Learning Model
Using Continuous Integration and Continuous Deployment
Managing a Model's Lifecycle
Read more
Syllabus
Course Overview
Deploying a Machine Learning Model
Using Continuous Integration and Continuous Deployment
Managing a Model's Lifecycle
Read more
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Taught by Jared Rhodes, who are recognized for their work in the field
Teaches skills, knowledge, and/or tools that are highly relevant in an academic setting
Develops professional skills or deep expertise in a particular topic or set of topics
May be too advanced for complete beginners
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Save Deploying and Managing Models in Microsoft Azure 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 Deploying and Managing Models in Microsoft Azure with these
activities:
Attend peer study sessions
Show steps
Collaborate with other learners to improve understanding and retention of course material.
Show steps
-
Schedule a regular study session with peers
-
Review course materials together
-
Discuss concepts and practice problems
Complete practice problems
Show steps
Reinforce theoretical concepts by applying them to practical problems.
Show steps
-
Identify relevant practice problems
-
Attempt to solve the problems самостоятельно
-
Review solutions and identify areas for improvement
Follow online Azure Machine Learning tutorials
Show steps
Supplement course material with additional practice and examples.
Browse courses on
Azure Machine Learning
Show steps
-
Search for relevant tutorials
-
Follow the step-by-step instructions
-
Apply what you've learned to your own projects
Four other activities
Expand to see all activities and additional details
Show all seven activities
Mentor junior learners in Azure Machine Learning
Show steps
Strengthen your understanding by teaching others and make a positive impact.
Show steps
-
Join online forums or mentorship platforms
-
Offer guidance and support to junior learners
-
Create resources or materials to help them along
Deploy a simple Azure Machine Learning model
Show steps
Gain practical experience in deploying and managing models.
Show steps
-
Choose a dataset and train a model
-
Deploy the model to Azure Machine Learning
-
Test and evaluate the deployed model
Develop a presentation on Azure Machine Learning best practices
Show steps
Solidify understanding and improve communication skills by sharing knowledge with others.
Show steps
-
Research Azure Machine Learning best practices
-
Create a presentation outline
-
Practice delivering the presentation
-
Present to peers or colleagues
Contribute to open-source Azure Machine Learning projects
Show steps
Deepen understanding, collaborate with others, and contribute to the wider community.
Browse courses on
Azure Machine Learning
Show steps
-
Find open-source Azure Machine Learning projects
-
Identify areas to contribute
-
Submit code contributions and participate in discussions
Attend peer study sessions
Show steps
Collaborate with other learners to improve understanding and retention of course material.
Show steps
Show steps
Show steps
- Schedule a regular study session with peers
- Review course materials together
- Discuss concepts and practice problems
Complete practice problems
Show steps
Reinforce theoretical concepts by applying them to practical problems.
Show steps
Show steps
Show steps
- Identify relevant practice problems
- Attempt to solve the problems самостоятельно
- Review solutions and identify areas for improvement
Follow online Azure Machine Learning tutorials
Show steps
Supplement course material with additional practice and examples.
Browse courses on
Azure Machine Learning
Show steps
Show steps
Show steps
- Search for relevant tutorials
- Follow the step-by-step instructions
- Apply what you've learned to your own projects
Four other activities
Expand to see all activities and additional details
Show all seven activities
Mentor junior learners in Azure Machine Learning
Show steps
Strengthen your understanding by teaching others and make a positive impact.
Show steps
Show steps
Show steps
- Join online forums or mentorship platforms
- Offer guidance and support to junior learners
- Create resources or materials to help them along
Deploy a simple Azure Machine Learning model
Show steps
Gain practical experience in deploying and managing models.
Show steps
Show steps
Show steps
- Choose a dataset and train a model
- Deploy the model to Azure Machine Learning
- Test and evaluate the deployed model
Develop a presentation on Azure Machine Learning best practices
Show steps
Solidify understanding and improve communication skills by sharing knowledge with others.
Show steps
Show steps
Show steps
- Research Azure Machine Learning best practices
- Create a presentation outline
- Practice delivering the presentation
- Present to peers or colleagues
Contribute to open-source Azure Machine Learning projects
Show steps
Deepen understanding, collaborate with others, and contribute to the wider community.
Browse courses on
Azure Machine Learning
Show steps
Show steps
Show steps
- Find open-source Azure Machine Learning projects
- Identify areas to contribute
- Submit code contributions and participate in discussions
Career center
Learners who complete Deploying and Managing Models in Microsoft Azure will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for building, deploying, and maintaining machine learning models. They work with data scientists to translate machine learning models into production-ready code. This course may be useful for Machine Learning Engineers who want to learn how to use Azure Machine Learning to deploy and manage machine learning models.
DevOps Engineer
DevOps Engineer
DevOps Engineers work with development and operations teams to automate the software development process. They use tools and techniques to streamline the development and deployment of software applications. This course may be useful for DevOps Engineers who want to learn how to use Azure DevOps to automate the deployment and management of machine learning models.
Data Science Manager
Data Science Manager
Data Science Managers lead and manage teams of data scientists. They work with stakeholders to define data science goals and objectives. This course may be useful for Data Science Managers who want to learn how to deploy and manage machine learning models in Azure.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers design and develop artificial intelligence systems. They work with data scientists and machine learning engineers to create intelligent systems. This course may be useful for Artificial Intelligence Engineers who want to learn how to deploy and manage machine learning models in Azure.
Machine Learning Scientist
Machine Learning Scientist
Machine Learning Scientists research and develop new machine learning algorithms. They work with data scientists to translate machine learning models into production-ready code. This course may be useful for Machine Learning Scientists who want to learn how to deploy and manage machine learning models in Azure.
Data Analyst
Data Analyst
Data Analysts use their skills in data analysis and visualization to communicate insights from data to stakeholders. They work with data to identify trends, patterns, and anomalies. This course may be useful for Data Analysts who want to learn how to deploy and manage machine learning models in Azure.
Project Manager
Project Manager
Project Managers plan, execute, and close projects. They work with stakeholders to define project goals and objectives. This course may be useful for Project Managers who want to learn how to deploy and manage machine learning models in Azure.
Data Engineer
Data Engineer
Data Engineers design and build data pipelines. They work with data to ensure that it is accurate, consistent, and accessible. This course may be useful for Data Engineers who want to learn how to deploy and manage machine learning models in Azure.
Cloud Architect
Cloud Architect
Cloud Architects design and implement cloud computing solutions. They work with cloud providers to create scalable and reliable cloud-based applications. This course may be useful for Cloud Architects who want to learn how to deploy and manage machine learning models in Azure.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts use their skills in data analysis and visualization to help businesses make better decisions. They work with data to identify trends, patterns, and anomalies. This course may be useful for Business Intelligence Analysts who want to learn how to deploy and manage machine learning models in Azure.
Product Manager
Product Manager
Product Managers work with customers and stakeholders to define and develop new products. They work with engineers and designers to bring new products to market. This course may be useful for Product Managers who want to learn how to deploy and manage machine learning models in Azure.
Data Scientist
Data Scientist
Data Scientists use their skills in programming, mathematics, and statistics to extract insights from data. They work with large datasets to identify trends, patterns, and anomalies. This course, Deploying and Managing Models in Microsoft Azure, may be useful for Data Scientists who want to learn how to deploy and manage machine learning models in Azure.
Business Analyst
Business Analyst
Business Analysts work with stakeholders to identify and solve business problems. They use their skills in data analysis and visualization to communicate insights to stakeholders. This course may be useful for Business Analysts who want to learn how to deploy and manage machine learning models in Azure.
Data Architect
Data Architect
Data Architects design and build data architectures. They work with data engineers to ensure that data is accurate, consistent, and accessible. This course may be useful for Data Architects who want to learn how to deploy and manage machine learning models in Azure.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They work with code to create new features and fix bugs. This course may be useful for Software Engineers who want to learn how to deploy and manage machine learning models in Azure.
For more career information including salaries, visit:
OpenCourser.com/course/azljev/deploying
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
Deploying and Managing Models in Microsoft Azure.
Save
Provides a comprehensive overview of the mathematics of machine learning, and it good choice for anyone who wants to learn more about the theory behind machine learning. It covers topics such as linear algebra, calculus, and optimization.
Provides a comprehensive overview of machine learning algorithms, and it good choice for anyone who wants to learn more about the theory behind machine learning. It covers topics such as supervised learning, unsupervised learning, and reinforcement learning.
Provides a comprehensive overview of natural language processing, and it good choice for anyone who wants to learn more about the theory and practice of natural language processing. It covers topics such as tokenization, stemming, lemmatization, parsing, and machine translation.
Provides a comprehensive overview of machine learning with R, and it good choice for anyone who wants to learn more about the theory and practice of machine learning with R. It covers topics such as data preparation, model training, and model evaluation.
Provides a comprehensive overview of deep learning, a subset of machine learning that uses artificial neural networks to learn from data.
Provides a hands-on approach to machine learning using popular Python libraries such as Scikit-Learn, Keras, and TensorFlow.
Provides a comprehensive overview of machine learning concepts and techniques, with a focus on practical applications.
Provides a concise overview of Azure Machine Learning, covering its features, benefits, and how to use it to build and deploy machine learning models.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/azljev/deploying
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Time
59 hours
Level
Advanced
Via
Pluralsight
Instructor
Jared Rhodes
Language
English
Good to know
Taught by Jared Rhodes, who are recognized for their work in the field
Teaches skills, knowledge, and/or tools that are highly relevant in an academic setting
Develops professional skills or deep expertise in a particular topic or set of topics
May be too advanced for complete beginners
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