The AI Study Guide: Azure Machine Learning Edition

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Hi! It's me, Natalie, your Azure AI and ML learning guru. Want to learn something about Azure AI or Machine Learning? I've got something for that!Hi! It's me, Natalie, your Azure AI and ML learning guru. Want to learn something about Azure AI or Machine Learning? I've got something for that!



The AI Study Guide: Discover Machine Learning with these free Azure resources 


Welcome to the February edition of the Azure AI Study Guide. Every month I’ll be spilling the tea on the best and newest tools for skilling up on Azure AI. This month we’re putting on our thinking caps to investigate Azure Machine Learning (ML). I’ll give you a quick breakdown of what it is, then we’ll explore a four-week roadmap of our top FREE resources for you to continue your AI learning journey! And as a bonus, stay tuned to the end to see what makes machine learning and generative AI a dynamic duo.  


First things first: What is Machine Learning? 

Short and sweet, Machine Learning is what happens when a computer learns from data without explicit programming. Algorithms help the computer improve its performance over time by analyzing data and identifying patterns, enabling it to make predictions, classifications, and decisions based on learned information. 


Meet Robo 

Imagine you have a pet robot named Robo. He’s cuddly and eager to please, but he didn’t come fully pre-programmed. So to make his owner—you—happy, he’s going to use machine learning to learn and adapt to his environment. 


Isn't Robo cute?Isn't Robo cute?


Say you want Robo to recognize different fruits so he can bring you a snack upon request. He has sensors that collect data about his surroundings, so to help you show him an apple or a banana. Robo examines each, and the algorithms within his software analyze the data.  These algorithms are like Robo’s "brain," capable of recognizing patterns and making connections. Now, when you show Robo a banana, he’ll say, “That looks like a banana!” Robo uses what he has learned from the seeing the different fruits to make decisions. It’s like Robo’s brain is figuring things out on its own!  


The learning process is continuous. As Robo interacts with you and the world, he gathers more data, feeds it back into the algorithms, and further refines his understanding. This creates a feedback loop that allows Robo to become more and more personalized and responsive over time. Eventually he’ll learn to not only distinguish produce, but also learn new tricks you teach him.  


Go from Azure Machine Learning novice to expert in 4 weeks. 

Now that we have a basic grasp on ML, let’s jump into the FREE 4-week ML roadmap I’ve built out for you below. This study guide is going to empower developers and data scientists alike to build, deploy, and manage high-quality models faster and with confidence.  


Week 1: Fundamentals  

Week 2: Get your hands dirty 

Week 3: Approaching expert status 

  • Deploy and consume models with Azure Machine Learning - Training | Microsoft Learn: You’ve trained your model—now, deploy it in your app! Enhance user experience with a service that enables real-time predictions for individual or small sets of data points. 
  • Video series: Scaling your AI/ML practices with MLOps and Azure Machine Learning: Join Seth Juarez and an amazing lineup of guests for a three-part series on powering AI and ML with Azure.  
  • Episode 1: Abe Omorogbe gives an overview of MLOps and how to utilize AzureML MLOps capabilities to streamline the process of moving ML experiments from training to inference. 
  • Episode 2: Setu Chokshi introduces the Azure MLOps (v2) Solution Accelerator and its value proposition. He also shares some customer use-cases! 
  • Episode 3: Scott Donohoo demos how to use the Azure MLOps Solution Accelerator to securely train, deploy and manage ML models in production environments.   

Week 4: Start training and earn a badge!   

  • Train and evaluate deep learning models: Strap in for some cutting-edge ML/AI. “Deep learning” is an advanced form of ML that tries to emulate the human brain, using artificial neural networks that process numeric inputs rather than electrochemical stimuli.  
  • Train a model and debug it with Responsible AI dashboard: Machine learning models should not only excel in accuracy but also adhere to ethical principles. In this module you’ll learn how to create a responsible AI dashboard, mitigate biases in your data, and meet compliance regulation requirements. 
  • Assessment: Train and deploy a machine learning model with Azure Machine Learning: OK, hotshot, time to show off! Demonstrate your ability to train and deploy ML models to earn a Microsoft Applied Skills credential (and a cool badge). 
  • Microsoft Certified: Azure Data Scientist Associate: This exam measures your ability to accomplish the following technical tasks: design and prepare a ML solution; explore data and train models; prepare a model for deployment; and deploy and retrain a model. (Note: Price for certification exam is based on the country or region in which the exam is proctored.)   


Extra goodies

Gen AI + ML = BFFs  

Generative AI and Azure Machine Learning are powerhouses individually, but together they create a synergy that unlocks even greater potential. For example, a common issue with training ML models is a lack of sufficient data. But generative AI can create synthetic data, allowing the ML model to learn from broader and more diverse datasets, improving its effectiveness and generalizability. 

To learn more about this dynamic duo, check out this series of videos produced by Azure experts:  

For these and more AI training content, visit our AI learning hub. See you next month! 

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