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Become a Machine Learning Engineer for Microsoft Azure

Nanodegree Program

Strengthen your machine learning skills and build practical experience by training, validating, and evaluating models using Azure Machine Learning.

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  • Estimated time
    3 Months

    At 5-10 hrs/week

  • Enroll by
    May 31, 2023

    Get access to classroom immediately on enrollment

  • Skills acquired
    Azure ML Pipelines, Azure Data Services, AI Business Context
In collaboration with
  • Microsoft

What you will learn

  1. Machine Learning Engineer for Microsoft Azure

    3 months to complete

    In this program, students will enhance their skills by building and deploying sophisticated machine learning solutions using popular open source tools and frameworks, and gain practical experience running complex machine learning tasks using the built-in Azure labs accessible inside the Udacity classroom.

    Prerequisite knowledge

    Prior experience with Python, Machine Learning, and Statistics

    1. Using Azure Machine Learning

      Machine learning is a critical business operation for many organizations. Learn how to configure machine learning pipelines in Azure, identify use cases for Automated Machine Learning, and use the Azure ML SDK to design, create, and manage machine learning pipelines in Azure.

    2. Machine Learning Operations

      This course covers a lot of the key concepts of operationalizing machine learning, from selecting the appropriate targets for deploying models, to enabling Application Insights, identifying problems in logs, and harnessing the power of Azure’s Pipelines. All these concepts are part of core DevOps pillars that will allow you to demonstrate solid skills for shipping machine learning models into production.

    3. Capstone Project

      The program capstone gives you the opportunity to use the knowledge you have obtained from this Nanodegree program to solve an interesting problem. You will have to use Azure’s Automated ML and HyperDrive to solve a task. Finally, you will have to deploy the model as a webservice and test the model endpoint.

All our programs include

  • Real-world projects from industry experts

    With real-world projects and immersive content built in partnership with top-tier companies, you’ll master the tech skills companies want.

  • Real-time support

    On demand help. Receive instant help with your learning directly in the classroom. Stay on track and get unstuck.

  • Career services

    You’ll have access to Github portfolio review and LinkedIn profile optimization to help you advance your career and land a high-paying role.

  • Flexible learning program

    Tailor a learning plan that fits your busy life. Learn at your own pace and reach your personal goals on the schedule that works best for you.

Program offerings

  • Class Content

    • Real-world projects
    • Project reviews
    • Project feedback from experienced reviewers
  • Student services

    • Student community
    • Real-time support
  • Career services

    • Github review
    • Linkedin profile optimization

Succeed with personalized services.

We provide services customized for your needs at every step of your learning journey to ensure your success.

Get timely feedback on your projects.

  • Personalized feedback
  • Unlimited submissions and feedback loops
  • Practical tips and industry best practices
  • Additional suggested resources to improve
  • 1,400+

    project reviewers

  • 2.7M

    projects reviewed

  • 88/100

    reviewer rating

  • 1.1 hours

    avg project review turnaround time

Learn with the best.

Learn with the best.

  • Noah Gift

    Founder of Pragmatic AI Labs

    Noah Gift teaches and consults at top universities and companies globally, including Duke and Northwestern. His areas of expertise are Machine Learning, MLOps, A.I., Data Science, and Cloud Architecture. Noah has authored several bestselling books, including <em>Python for DevOps</em>.

  • Alfredo Deza


    Alfredo Deza is a passionate software engineer, avid open source developer, Vim plugin author, photographer, and former Olympic athlete. He has rebuilt company infrastructure, designed shared storage, and replaced complex build systems, always in search of efficient and resilient environments.

  • Erick Galinkin

    Principal AI Researcher | Rapid7

    Erick Galinkin is a hacker and computer scientist, leading research at the intersection of security and artificial intelligence at Rapid7. He has spoken at numerous industry and academic conferences on topics ranging from malware development to game theory in security.

  • Soham Chatterjee

    Graduate Student at the Nanyang Technological University

    Soham is an Intel® Software Innovator and a former Deep Learning Researcher at Saama Technologies. He is currently a Masters by Research student at NTU, Singapore. His research is on Edge Computing, IoT and Neuromorphic Hardware.

Machine Learning Engineer for Microsoft Azure

Get started today

    • Learn

      Strengthen your machine learning skills and gain practical experience by training, validating, and evaluating machine learning models for Microsoft Azure.

    • Average Time

      On average, successful students take 3 months to complete this program.

    • Benefits include

      • Real-world projects from industry experts
      • Real-time classroom support
      • Career services

    Program details

    Program overview: Why should I take this program?
    • Why should I enroll?

      Businesses everywhere are mobilizing to create and implement the AI strategies that will transform industries in coming years, and they need engineers to do it. Data from LinkedIn indicates that AI specialists are among the most sought after roles that companies are looking for, with a 74 percent annual growth rate in hiring over the last four years. To stay in-demand at companies on the cutting edge of technology, engineers should prioritize developing their machine learning skill set.

      The Machine Learning Engineer for Microsoft Azure Nanodegree Program, built in collaboration with Microsoft, offers you the chance to build the practitioner-level skills that companies across industries need. In the program, you’ll strengthen your machine learning skills by training, validating, and evaluating models using Azure Machine Learning, and complete a series of three real-world projects to add to your portfolio.

    • What jobs will this program prepare me for?

      Students in the program will learn about machine learning algorithms and crucial deployment techniques, and will be equipped to fill roles at companies seeking machine learning engineers and AI specialists. These skills can also be applied in roles at companies that are looking for data scientists to introduce machine learning techniques into their organization.

    • How do I know if this program is right for me?

      The Machine Learning Engineer for Microsoft Azure Nanodegree program is geared towards people who are interested in building and deploying a machine learning product or application. The program is a good fit for...

      • Data scientists who are trying to expand their knowledge and application of ML techniques
      • Software Developers who want to add ML concepts and techniques into their toolkit, or use Microsoft Azure for ML model development
      • Other professionals who understand ML foundations but want to deepen their knowledge of and experience with practical applications of ML skills
    Enrollment and admission
    • Do I need to apply? What are the admission criteria?

      There is no application. This Nanodegree program accepts everyone, regardless of experience and specific background.

    • What are the prerequisites for enrollment?

      A well-prepared learner will meet the following prerequisites:

      • Experience with basic Python programming (e.g., ability to read and write simple Python scripts; understanding of introductory concepts like variables, loops, modules, conditionals, data types, and functions).
      • Some experience with fundamental statistics and algebra, including an understanding of data distributions (e.g., normal distribution) measures of central tendency and variability (e.g., mean and standard deviation) and basic linear equations.
      • Udacity also recommends basic familiarity with fundamental machine learning concepts (such as feature engineering and supervised vs. unsupervised learning) and classic machine learning algorithms (such as linear regression and k-means clustering).
      • An understanding of the basics of Azure and Docker/Container experience.
      • If you'd like to prepare for this Nanodegree program, check out our Introduction to Machine Learning and AI Programming with Python courses.
    • If I do not meet the requirements to enroll, what should I do?

      To prepare, we recommend the Introduction to Machine Learning and AI Programming with Python programs, to build your comfortability with ML concepts and using python in an AI context.

    Tuition and term of program
    • How is this Nanodegree program structured?

      The Machine Learning Engineer for Microsoft Azure Nanodegree program is comprised of content and curriculum to support three (3) projects. We estimate that students can complete the program in three (3) months working 5-10 hours per week.

      Each project will be reviewed by the Udacity reviewer network. Feedback will be provided and if you do not pass the project, you will be asked to resubmit the project until it passes.

    • How long is this Nanodegree program?

      Access to this Nanodegree program runs for the length of time specified above. If you do not graduate within that time period, you will continue learning with month-to-month payments. See the Terms of Use and FAQs for other policies regarding the terms of access to our Nanodegree programs.

    • Can I switch my start date? Can I get a refund?

      Please see the Udacity Program FAQs for policies on enrollment in our programs.

    Software and hardware: What do I need for this program?
    • What software and versions will I need for this program?

      For this program, you will need a desktop or laptop computer running recent versions of Windows, Mac OS X, or Linux, and an unmetered broadband Internet connection. There are no additional hardware or software requirements for this program, other than those outlined on Udacity's general Technology Requirements page.