Managing Microsoft Azure AI Solutions from Pluralsight

This course shows you how to manage AI solutions in Azure. It explains how you can use ML Ops, monitor and collect data in production AKS clusters, and automate the entire process end to end

Companies and Governments across the globe are pouring billions of dollars into AI. The projects are getting ever more interesting and complex, and it is therefore natural to conclude that these projects need management.

In this course, Managing Microsoft Azure AI Solutions, I assert that an AI project is like any other software project, and the need to manage it with good software practices is more, not less, important. With demos, you'll learn how you can use concepts such as Azure CLI, ML SDK, and ML Ops to fully automate your end to end process. You'll also explore how you can set up an Azure DevOps pipeline to go from experiment to a service. But the fun doesn't end there; you'll then discover how to deploy your model as an AKS cluster and enable data monitoring and collection in production, so you can use that data in numerous ways to analyze it or feed it back into your model for subsequent improvement.

By the end of this course, you'll have an in-depth understanding of how to manage your AI projects like a proper software project. Concepts such as ML Ops and Pipelines will be second nature to you, and you'll be a pro at collecting and monitoring your production AI solutions.

What's inside

Syllabus

Course Overview

Managing Models in Azure Machine Learning Service

Registering Your Model

Registering and Deploying Your Image

Monitoring and Improving Data and Models

Wrapping Up

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Provides foundational knowledge on AI project management, including important concepts like ML Ops and pipelines

Emphasizes the importance of managing AI projects like any other software project, highlighting the significance of good software practices

Involves hands-on demonstrations and real-world examples, making the learning process more engaging and practical

Covers a range of topics, from registering and deploying models to monitoring and improving data and models, providing a comprehensive understanding of AI project management

Facilitates the deployment of models as AKS clusters, enabling data monitoring and collection in production for further analysis and model improvement

Instructor Sahil Malik is recognized for their expertise in AI and ML

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 Managing Microsoft Azure AI Solutions with these activities:

Organize and Review Course Materials

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Enhance your comprehension and retention by organizing and reviewing course materials regularly.

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Gather and organize lecture notes, assignments, quizzes, and exams.
Review the materials to reinforce your understanding.

Review Azure CLI Basic Commands

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Build a stronger foundation for managing Azure AI solutions by reviewing the basics of Azure CLI.

Browse courses on Command Line Interface

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Go through the Microsoft documentation for Azure CLI basics.
Practice using the Azure CLI in a sandbox or local environment.

Hands-on Practice with ML SDK

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Reinforce your understanding of ML SDK by engaging in practical exercises.

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Find tutorials or documentation that provide hands-on exercises with ML SDK.
Set up a development environment and follow the instructions to complete the exercises.

Five other activities

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Explore Azure DevOps Pipeline

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Deepen your understanding of Azure DevOps by following guided tutorials.

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Search for tutorials or documentation that provide a step-by-step guide to Azure DevOps Pipeline.
Follow the instructions in the tutorial to set up and use Azure DevOps Pipeline.

Develop an ML Ops Pipeline

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Demonstrate your mastery of ML Ops by creating a complete pipeline.

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Plan and design the ML Ops pipeline.
Implement the pipeline using Azure tools and services.
Test and evaluate the pipeline's performance.

Gather Resources on Data Monitoring and Collection in Production

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Expand your knowledge by compiling resources on data monitoring and collection in production.

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Search for articles, whitepapers, and documentation on data monitoring and collection.
Organize and curate the gathered resources into a central location.

Seek Guidance from Azure AI Experts

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Enhance your understanding by connecting with experienced professionals in the field.

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Identify potential mentors who have expertise in Azure AI.
Reach out to mentors and schedule meetings or discussions to seek guidance.

Contribute to Azure AI Open Source Projects

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Gain practical experience and expand your network by contributing to open source projects related to Azure AI.

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Identify open source projects aligned with your interests and skills.
Review the project documentation and identify areas where you can contribute.
Make code contributions, report bugs, or participate in discussions.

Career center

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

Data Science Manager

Data Science Managers lead teams of data scientists and engineers in the development and deployment of AI solutions. This course will teach you how to manage AI solutions in Azure, making you a more effective Data Science Manager.

See salaries and explore the career path for Data Science Manager

Machine Learning Engineer

Machine Learning Engineers design, develop, test, deploy, maintain, and manage machine learning systems. This course, Managing Microsoft Azure AI Solutions, will teach you how to manage AI solutions in Azure, including end to end automation, ML Ops, and data monitoring and collection in production AKS clusters.

See salaries and explore the career path for Machine Learning Engineer

AI Engineer

AI Engineers design, develop, test, deploy, and manage AI systems. This course, Managing Microsoft Azure AI Solutions, can help aspiring AI Engineers learn how to manage AI solutions in Azure, including how to use concepts like ML Ops, pipelines, and data monitoring and collection in production AKS clusters.

See salaries and explore the career path for AI Engineer

Data Engineer

Data Engineers design, build, and maintain the infrastructure and tools that are used to store, process, and analyze data. Managing Microsoft Azure AI Solutions can be useful for aspiring Data Engineers as it teaches how to use concepts like ML Ops and Azure DevOps pipelines to go from experiment to a service.

See salaries and explore the career path for Data Engineer

Solutions Architect

Solutions Architects design, build, and manage complex technology solutions. This course will teach you how to manage AI solutions in Azure, making you a more effective Solutions Architect in the growing field of AI.

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Cloud Architect

Cloud Architects design, build, and manage cloud computing systems. This course will teach you how to manage AI solutions in Azure, making you a more effective Cloud Architect in the growing field of AI.

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DevOps Engineer

DevOps Engineers integrate development and operations teams to improve the efficiency and quality of software development and delivery. Managing Microsoft Azure AI Solutions can be useful for aspiring DevOps Engineers as it teaches how to use Azure DevOps pipelines to go from experiment to a service.

See salaries and explore the career path for DevOps Engineer

Data Scientist

Data Scientists use their expertise in mathematics, statistics, and programming to collect, analyze, interpret, and present complex data. With the skills learned in Managing Microsoft Azure AI Solutions, you'll be able to manage the full ML lifecycle of your AI projects, including monitoring, improving, and collecting data, as well as deploying your models as an AKS cluster. This course may be useful for aspiring Data Scientists.

See salaries and explore the career path for Data Scientist

Data Analyst

Data Analysts collect, process, and analyze data from a variety of sources to identify trends, patterns, and other useful information. Managing Microsoft Azure AI Solutions can be useful for aspiring Data Analysts as it teaches how to use concepts like Azure CLI, ML SDK, and ML Ops to fully automate an end to end process.

See salaries and explore the career path for Data Analyst

Business Intelligence Analyst

Business Intelligence Analysts use data to identify trends and patterns, and to make recommendations to improve business performance. This course may be helpful for aspiring Business Intelligence Analysts as it teaches how to use concepts like Azure CLI, ML SDK, and ML Ops to fully automate an end to end process.

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Software Engineer

Software Engineers apply engineering principles to the design, development, implementation, testing, and maintenance of software systems. Managing Microsoft Azure AI Solutions will help you learn how to manage AI solutions in Azure, making you a more effective software engineer in the growing field of AI.

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Entrepreneur

Entrepreneurs start and run their own businesses. Managing Microsoft Azure AI Solutions may be useful for aspiring Entrepreneurs as it teaches how to manage the full ML lifecycle of AI projects, including monitoring, improving, and collecting data. This course also provides a foundation in Azure DevOps pipelines, which can help you automate the development and deployment of your AI solutions. However, this course focuses on Azure, so it may be less useful if you are planning to build an AI solution on another platform.

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Product Manager

Product Managers are responsible for the vision, strategy, and execution of products. Managing Microsoft Azure AI Solutions may be useful for aspiring Product Managers as it teaches how to manage the full ML lifecycle of AI projects, including monitoring, improving, and collecting data.

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Consultant

Consultants provide advice and guidance to businesses on a variety of topics, including AI. Managing Microsoft Azure AI Solutions may be useful for aspiring Consultants as it teaches how to manage the full ML lifecycle of AI projects, including monitoring, improving, and collecting data.

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Research Scientist

Research Scientists conduct research in a variety of fields, including AI. Managing Microsoft Azure AI Solutions may be useful for aspiring Research Scientists as it teaches how to manage the full ML lifecycle of AI projects, including monitoring, improving, and collecting data.

See salaries and explore the career path for Research Scientist