Operationalizing Microsoft Azure AI Solutions from Pluralsight

In Microsoft Azure, models can be created with Azure Databricks and Azure Machine Learning (ML). In this course we will focus on operationalizing our AI Model with Microsoft Azure's version control platform, Azure DevOps.

Machine learning and the Azure Artificial Intelligence (AI) platform allow you to use existing data to forecast future behaviors, outcomes, and trends. In this course, Operationalizing Microsoft Azure AI Solutions, you’ll focus on what it means to operationalize AI in Microsoft Azure. First, you’ll go through the lifecycle of an AI model, creating the model in Azure Databricks and Azure Machine Learning Services. Next, you’ll discover how to deploy the model into Azure’s version control tool, Azure Devops, and containerize it such that it can be used by the end user. Finally, you’ll explore how to identify integration points with other Microsoft services and the containers used in Azure - Azure Container Instances and Azure Kubernetes Services. When you’re finished with this course, you’ll have the skills and knowledge of Microsoft Azure needed to devise a strategy for managing version control of an AI solution.

What's inside

Syllabus

Course Overview

Assembling Appropriate Tools and Technologies

Designing a Version Control Strategy for a Microsoft Azure AI Solution

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Emphasizes version control within AI solutions, a crucial aspect for managing complex deployments

Taught by experienced instructor Dayo Bamikole, known for expertise in Azure AI and DevOps

Covers the integration of other Microsoft services, enhancing the course's practical relevance

Focuses on Azure DevOps for version control, a widely recognized industry standard

Provides a structured approach to operationalizing AI models, addressing a key challenge in AI deployment

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 Operationalizing Microsoft Azure AI Solutions with these activities:

Review Azure Databricks and Azure Machine Learning

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Refreshing your skills in Azure Databricks and Azure Machine Learning will provide you with a strong foundation for operationalizing AI models with Azure DevOps.

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Review the basics of Azure Databricks and Azure Machine Learning
Complete hands-on labs or tutorials on Azure Databricks and Azure Machine Learning
Experiment with creating and deploying simple machine learning models using Azure Databricks and Azure Machine Learning

Create a GitHub repository and use Azure DevOps to manage it

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Practical experience with Azure DevOps and GitHub will enhance your understanding of version control and collaboration.

Browse courses on Azure DevOps

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Create a GitHub repository
Connect the repository to an Azure DevOps project
Use Azure DevOps to manage commits, branches, and merge requests

Practice Azure DevOps concepts using online tutorials

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Applying Azure DevOps concepts through practice drills will reinforce your understanding of version control and deployment processes.

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Identify relevant Azure DevOps tutorials
Follow the tutorials step-by-step, implementing the concepts in your own environment
Troubleshoot any errors you encounter during the exercises

Ten other activities

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Follow tutorials on operationalizing AI models with Azure DevOps

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Following tutorials will provide you with a structured learning path and hands-on experience in operationalizing AI models with Azure DevOps.

Browse courses on Azure DevOps

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Identify relevant tutorials from Microsoft Learn, Pluralsight, or other platforms
Follow the tutorials step-by-step
Implement the concepts in your own Azure DevOps environment

Join a study group to discuss AI model deployment strategies

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Engaging in peer discussions about AI model deployment will expand your understanding and provide different perspectives.

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Find or create a study group
Prepare topics for discussion
Participate actively in the group discussions
Share knowledge and learn from others

Practice deploying models with Azure DevOps

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By practicing deploying models with Azure DevOps, you will solidify your understanding of the deployment process and enhance your ability to deploy models effectively.

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Create a model in Azure Databricks or Azure Machine Learning
Configure Azure DevOps to integrate with Azure Machine Learning
Use Azure DevOps to create a build pipeline for model deployment
Test the deployment pipeline to ensure successful model deployment

Volunteer with an organization that uses Azure DevOps for AI model deployment

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Volunteering will provide you with hands-on experience and the opportunity to learn from experts in the field of operationalizing AI models with Azure DevOps.

Browse courses on Azure DevOps

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Identify and contact an organization that uses Azure DevOps for AI model deployment
Inquire about volunteer opportunities and express your interest
Contribute your skills and knowledge to the organization's projects

Create a detailed report on best practices for operationalizing AI models

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Documenting your understanding of best practices will solidify your knowledge and provide a valuable resource for future reference.

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Research best practices for AI model operationalization
Analyze case studies and industry examples
Write a comprehensive report outlining the best practices

Attend a workshop on operationalizing AI models with Azure DevOps

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Attending a workshop will provide you with hands-on experience and expert guidance in operationalizing AI models with Azure DevOps.

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Identify and register for a relevant workshop
Attend the workshop and actively participate in the exercises
Engage with the instructors and other attendees to learn from their experiences

Participate in an AI hackathon

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Applying your knowledge in a competitive environment will test your skills and enhance your understanding of AI model deployment.

Browse courses on Problem-Solving

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Find an appropriate AI hackathon
Form a team or join an existing one
Develop a plan and divide responsibilities
Implement your solution and prepare for the presentation

Create a blog post or video tutorial on operationalizing AI models with Azure DevOps

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Creating a blog post or video tutorial will not only reinforce your understanding but also contribute to the community knowledge base and help others learn about operationalizing AI models with Azure DevOps.

Browse courses on Azure DevOps

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Choose a specific topic related to operationalizing AI models with Azure DevOps
Research and gather information on the topic
Write or record the content in a clear and engaging manner
Publish your blog post or video tutorial

Contribute to an open-source project related to operationalizing AI models with Azure DevOps

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Contributing to an open-source project will allow you to learn from others, contribute to the community, and gain practical experience in operationalizing AI models with Azure DevOps.

Browse courses on Azure DevOps

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Identify a relevant open-source project
Review the project's documentation and code
Identify areas where you can contribute
Submit your contributions and engage with the project maintainers

Participate in a hackathon or competition on AI model deployment

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Participating in a hackathon or competition will provide you with a challenging and immersive learning experience in operationalizing AI models with Azure DevOps.

Browse courses on Azure DevOps

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Identify and register for a relevant hackathon or competition
Form a team or work individually on the project
Develop and deploy an AI model using Azure DevOps
Submit your project and present it to the judges

Career center

Learners who complete Operationalizing Microsoft Azure AI Solutions will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists are responsible for collecting, analyzing, and interpreting data to help businesses make informed decisions and improve operations. The Operationalizing Microsoft Azure AI Solutions course can help you build the skills you need to succeed in this role by teaching you how to create, deploy, and manage AI models using Azure Databricks, Azure Machine Learning, and Azure DevOps. This course will also help you develop the skills you need to integrate AI solutions with other Microsoft services and containerize them for use by end users.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers design, develop, and deploy machine learning models to solve real-world problems. The Operationalizing Microsoft Azure AI Solutions course can help you build the skills you need to succeed in this role by teaching you how to create, deploy, and manage AI models using Azure Databricks, Azure Machine Learning, and Azure DevOps. This course will also help you develop the skills you need to integrate AI solutions with other Microsoft services and containerize them for use by end users.

See salaries and explore the career path for Machine Learning Engineer

Data Analyst

Data Analysts collect, analyze, and interpret data to help businesses understand their customers, improve operations, and make better decisions. The Operationalizing Microsoft Azure AI Solutions course can help you build the skills you need to succeed in this role by teaching you how to create, deploy, and manage AI models using Azure Databricks, Azure Machine Learning, and Azure DevOps. This course will also help you develop the skills you need to integrate AI solutions with other Microsoft services and containerize them for use by end users.

See salaries and explore the career path for Data Analyst

DevOps Engineer

DevOps Engineers are responsible for bridging the gap between development and operations teams to ensure that software is developed, deployed, and maintained efficiently and reliably. The Operationalizing Microsoft Azure AI Solutions course can help you build the skills you need to succeed in this role by teaching you how to create, deploy, and manage AI models using Azure Databricks, Azure Machine Learning, and Azure DevOps. This course will also help you develop the skills you need to integrate AI solutions with other Microsoft services and containerize them for use by end users.

See salaries and explore the career path for DevOps Engineer

Software Engineer

Software Engineers design, develop, and maintain software applications. The Operationalizing Microsoft Azure AI Solutions course can help you build the skills you need to succeed in this role by teaching you how to create, deploy, and manage AI models using Azure Databricks, Azure Machine Learning, and Azure DevOps. This course will also help you develop the skills you need to integrate AI solutions with other Microsoft services and containerize them for use by end users.

See salaries and explore the career path for Software Engineer

Data Engineer

Data Engineers design, develop, and maintain data pipelines to ensure that data is available and accessible for analysis. The Operationalizing Microsoft Azure AI Solutions course can help you build the skills you need to succeed in this role by teaching you how to create, deploy, and manage AI models using Azure Databricks, Azure Machine Learning, and Azure DevOps. This course will also help you develop the skills you need to integrate AI solutions with other Microsoft services and containerize them for use by end users.

See salaries and explore the career path for Data Engineer

Cloud Architect

Cloud Architects design, develop, and manage cloud computing solutions. The Operationalizing Microsoft Azure AI Solutions course can help you build the skills you need to succeed in this role by teaching you how to create, deploy, and manage AI models using Azure Databricks, Azure Machine Learning, and Azure DevOps. This course will also help you develop the skills you need to integrate AI solutions with other Microsoft services and containerize them for use by end users.

See salaries and explore the career path for Cloud Architect

Product Manager

Product Managers are responsible for developing and managing products and services. The Operationalizing Microsoft Azure AI Solutions course can help you build the skills you need to succeed in this role by teaching you how to create, deploy, and manage AI models using Azure Databricks, Azure Machine Learning, and Azure DevOps. This course will also help you develop the skills you need to integrate AI solutions with other Microsoft services and containerize them for use by end users.

See salaries and explore the career path for Product Manager

Business Analyst

Business Analysts help businesses understand their customers, improve operations, and make better decisions. The Operationalizing Microsoft Azure AI Solutions course can help you build the skills you need to succeed in this role by teaching you how to create, deploy, and manage AI models using Azure Databricks, Azure Machine Learning, and Azure DevOps. This course will also help you develop the skills you need to integrate AI solutions with other Microsoft services and containerize them for use by end users.

See salaries and explore the career path for Business Analyst

Project Manager

Project Managers plan, execute, and manage projects to ensure that they are completed on time, within budget, and to the required quality. The Operationalizing Microsoft Azure AI Solutions course can help you build the skills you need to succeed in this role by teaching you how to create, deploy, and manage AI models using Azure Databricks, Azure Machine Learning, and Azure DevOps. This course will also help you develop the skills you need to integrate AI solutions with other Microsoft services and containerize them for use by end users.

See salaries and explore the career path for Project Manager

IT Manager

IT Managers are responsible for planning, implementing, and managing IT systems and services. The Operationalizing Microsoft Azure AI Solutions course can help you build the skills you need to succeed in this role by teaching you how to create, deploy, and manage AI models using Azure Databricks, Azure Machine Learning, and Azure DevOps. This course will also help you develop the skills you need to integrate AI solutions with other Microsoft services and containerize them for use by end users.

See salaries and explore the career path for IT Manager

Data Administrator

Data Administrators are responsible for managing and maintaining data systems and databases. The Operationalizing Microsoft Azure AI Solutions course can help you build the skills you need to succeed in this role by teaching you how to create, deploy, and manage AI models using Azure Databricks, Azure Machine Learning, and Azure DevOps. This course will also help you develop the skills you need to integrate AI solutions with other Microsoft services and containerize them for use by end users.

See salaries and explore the career path for Data Administrator

Database Administrator

Database Administrators are responsible for managing and maintaining databases. The Operationalizing Microsoft Azure AI Solutions course can help you build the skills you need to succeed in this role by teaching you how to create, deploy, and manage AI models using Azure Databricks, Azure Machine Learning, and Azure DevOps. This course will also help you develop the skills you need to integrate AI solutions with other Microsoft services and containerize them for use by end users.

See salaries and explore the career path for Database Administrator

IT Analyst

IT Analysts provide technical support to users and troubleshoot and resolve IT issues. The Operationalizing Microsoft Azure AI Solutions course may be useful for you if you are interested in a career as an IT Analyst by teaching you how to create, deploy, and manage AI models using Azure Databricks, Azure Machine Learning, and Azure DevOps.

See salaries and explore the career path for IT Analyst

Help Desk Technician

Help Desk Technicians provide technical support to users and troubleshoot and resolve IT issues. The Operationalizing Microsoft Azure AI Solutions course may be useful for you if you are interested in a career as a Help Desk Technician by teaching you how to create, deploy, and manage AI models using Azure Databricks, Azure Machine Learning, and Azure DevOps.

See salaries and explore the career path for Help Desk Technician

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 Operationalizing Microsoft Azure AI Solutions.

Azure PowerShell Quick Start Guide

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The book comprehensive guide to machine learning with Azure. It covers the basics of machine learning, such as data preparation, model training, and model deployment. It also provides guidance on how to use Azure Machine Learning to build and deploy machine learning models.

Azure PowerShell Quick Start Guide: Deploy and...

Kindle Edition

Deep Learning with Python, Second Edition

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Is an introduction to deep learning using Python. It covers the basics of deep learning, such as neural networks, convolutional neural networks, and recurrent neural networks. It also provides guidance on how to use Python libraries such as TensorFlow and Keras to build and train deep learning models.

Deep Learning with Python, Second Edition

Paperback

Deep Learning with Python, Second Edition

Kindle Edition

The The DevOps 2.2 Toolkit

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Is an introduction to Docker. It covers the basics of Docker, such as images, containers, and volumes. It also provides guidance on how to use Docker to build and deploy containerized applications.

The The DevOps 2.2 Toolkit

Paperback

Check price

The The DevOps 2.2 Toolkit

Kindle Edition

Check price

Machine Learning with PyTorch and Scikit-Learn

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Provides a practical introduction to machine learning using Python. It covers the basics of machine learning, such as data preparation, model training, and model evaluation. It good choice for readers who want to learn how to use machine learning to solve real-world problems.

Machine Learning with PyTorch and Scikit-Learn:...

Kindle Edition

Exam Ref 70-532 Developing Microsoft Azure Solutions

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The book covers the practice of continuous delivery and how it can be implemented using Azure DevOps. It provides guidance on how to automate the build, test, and deployment of software using Azure DevOps.

Exam Ref 70-532 Developing Microsoft Azure Solutions

Paperback

Designing Data-Intensive Applications

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Provides theoretical and practical knowledge about designing data-intensive applications. It covers topics such as data modeling, data storage, and data processing. The book is useful for anyone who wants to build scalable and efficient data-intensive applications.

Designing Data-Intensive Applications: The Big...

Paperback

Designing Data-Intensive Applications: The Big...

Kindle Edition

Operationalizing Microsoft Azure AI Solutions

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