Optimizing Microsoft Azure AI Solutions from Pluralsight

Microsoft's cloud-based platform Azure provides multiple AI services such as AzureML Compute Cluster, Azure HDInsight, Azure Databricks, Azure DevOps. In this course you will learn how to design, deploy, and optimize applications built with Microsoft AI Solutions.

Cloud-based platform Microsoft Azure has multiple AI services which could be used to train your model for big data sets as well as to deploy your model as a web service. In this course, Optimizing Microsoft Azure AI Solutions, you will learn the foundational knowledge of how to train your machine learning models using Azure's services such AzureML Compute Cluster, Azure HDInsight, Azure Databricks, and Azure Data Science Virtual Machine. Next, you will discover how to optimize your storage by using Azure Premium blob storage service and data formats such as Pickle and Parquet. Finally, you will explore how to scale your machine learning models and manage end-to-end machine learning life cycle using the principle of MLOps. When you’re finished with this course, you will have the skills and knowledge of Mircosoft Azure's core AI services needed to design, deploy, and optimize your model.

What's inside

Syllabus

Course Overview

Optimizing Core Services

Optimizing Storage and Logging

Optimizing Deployments and Operations

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops learning paths that clearly map out steps to achieve objectives

Emphasizes strategies in cloud-based platform Microsoft Azure for building AI solutions

focuses on implementation and optimization of AI solutions with Azure services

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 Optimizing Microsoft Azure AI Solutions with these activities:

Review Data Analysis and Modeling Techniques

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Review your data analysis and modeling techniques to ensure you have a strong foundation for building and optimizing AI solutions on Azure.

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Review statistical concepts such as mean, median, mode, and standard deviation
Practice using data visualization tools to explore and analyze data
Review machine learning algorithms such as linear regression, logistic regression, and decision trees

Review machine learning concepts

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Brush up on the fundamentals of machine learning to strengthen your understanding of the core concepts covered in this course.

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Go over your notes and assignments from previous machine learning courses.
Work through practice problems and exercises related to machine learning concepts.
Review online resources and tutorials on machine learning.

Learn about Azure HDInsight and Databricks

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Gain hands-on experience with Azure services for big data analytics to prepare you for building and optimizing AI solutions on Azure.

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Follow tutorials to set up an Azure HDInsight cluster
Explore Azure Databricks and learn how to use it for data exploration and modeling

Nine other activities

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Complete Azure ML Compute Cluster tutorial

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Gain hands-on experience with Azure ML Compute Cluster to better understand its functionality and how to use it effectively.

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Follow the official Azure ML Compute Cluster tutorial.
Set up your Azure environment and create an ML Compute Cluster.
Run a machine learning training job on the cluster.

Solve Azure HDInsight practice problems

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Test your understanding of Azure HDInsight and enhance your problem-solving skills through targeted practice.

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Find online resources or textbooks with Azure HDInsight practice problems.
Solve as many problems as possible, focusing on different aspects of HDInsight.
Review your solutions and identify areas for improvement.

Practice using AzureML Compute Cluster for training ML models

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Reinforce your understanding of training machine learning models on Azure by completing practice exercises.

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Create an AzureML Compute Cluster
Train a machine learning model using the cluster

Join a study group for Azure DevOps

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Enhance your understanding of Azure DevOps by collaborating with peers and discussing real-world scenarios.

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Find a study group or create one with classmates
Meet regularly to discuss Azure DevOps concepts and work on projects together

Write a blog post on Azure Databricks

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Reinforce your understanding of Azure Databricks by creating a blog post that explains its key features and how to use it.

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Research Azure Databricks and gather relevant information.
Outline the structure of your blog post and write the content.
Edit and proofread your post carefully.
Publish your blog post on a platform of your choice.

Contribute to an Azure DevOps project

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Gain practical experience with Azure DevOps and contribute to the open-source community by collaborating on a project.

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Identify an open-source Azure DevOps project that aligns with your interests.
Review the project's documentation and contribute code or documentation.
Engage with the project maintainers and other contributors.

Create a presentation on optimizing storage and logging in Azure AI Solutions

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Deepen your understanding of storage and logging optimization by creating a presentation on the topic.

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Research best practices for optimizing storage and logging in Azure AI Solutions
Create a presentation that explains these best practices and provides examples

Participate in a hackathon focused on AI solutions

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Challenge yourself and apply your skills by participating in a hackathon centered around AI solutions.

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Find a hackathon that aligns with your interests
Form a team or work individually to develop an innovative AI solution

Contribute to an open-source project related to Azure AI Solutions

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Gain practical experience and contribute to the Azure AI community by participating in open-source projects.

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Find an open-source project that interests you
Contribute to the project by fixing bugs, adding features, or improving documentation

Career center

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

Machine Learning Engineer

Machine Learning Engineers design, build, and maintain machine learning models. They use their knowledge of mathematics, statistics, and computer science to create models that can learn from data and make predictions. This course would be particularly helpful for Machine Learning Engineers who are looking to learn how to use Microsoft Azure's AI services, such as AzureML Compute Cluster, Azure HDInsight, and Azure Databricks, to optimize their models.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

Data Scientists use machine learning, statistics, and data mining to extract insights from data. They use this information to solve business problems and make predictions. This course could be helpful for Data Scientists who want to learn how to use Microsoft Azure's AI services to manage the end-to-end machine learning life cycle.

See salaries and explore the career path for Data Scientist

Software Engineer

Software Engineers design, develop, and maintain software applications. They use their knowledge of programming languages and software development tools to create software that meets the needs of users. This course could be helpful for Software Engineers who want to learn how to develop and deploy machine learning models using Microsoft Azure's AI services.

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Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze data and make predictions. They use this information to make investment decisions and manage risk. This course could be helpful for Quantitative Analysts who want to learn how to use Microsoft Azure's AI services to develop and deploy machine learning models.

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Business Analyst

Business Analysts use data analysis and modeling to identify and solve business problems. They use this information to make recommendations to decision-makers. This course could be helpful for Business Analysts who want to learn how to use Microsoft Azure's AI services to develop and deploy machine learning models that can be used to solve business problems.

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Data Analyst

Data Analysts use data analysis and modeling to identify and solve business problems. They use this information to make recommendations to decision-makers.

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Statistician

Statisticians use mathematical and statistical models to analyze data and make predictions. They use this information to solve problems in a variety of fields, including medicine, finance, and marketing.

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Operations Research Analyst

Operations Research Analysts use mathematical and statistical models to improve the efficiency of operations. They use this information to make recommendations to decision-makers.

See salaries and explore the career path for Operations Research Analyst