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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.

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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.

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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
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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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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
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
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
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
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
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
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
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

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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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.

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.
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 deep learning, a subset of machine learning that uses artificial neural networks to learn from data.
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.

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