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.
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.
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.
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.
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.
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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