AI-900: Azure AI Fundamentals: Evaluating Models with the ML Designer

Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level

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Overview/Description
In order to build a powerful and useful machine learning deployment, you must be able to evaluate and verify the AI model and data, as well as the accuracy and effectiveness of its predictions. Azure Machine Learning Studio and the Designer provide multiple easy-to-use methods for evaluating and scoring a model. In this course, you'll learn how to score and evaluate models and interpret and evaluate the results from some common models. You'll also explore how to create an inference pipeline, add web service output to provide external access to the model, and deploy and test a predictive web service. This course is one of a collection that prepares learners for the Microsoft Azure AI Fundamentals (AI-900) exam.

Expected Duration (hours)
2.0

Lesson Objectives

AI-900: Azure AI Fundamentals: Evaluating Models with the ML Designer

discover the key concepts covered in this course

add a Scoring model component in the ML Designer

describe model evaluation types like MAE and R2

use an evaluator on a model and interpret the metrics

run and monitor a complete pipeline

analyze the evaluation results in the output and logs section in the ML Designer

identify and investigate the details of the evaluation results

visualize the scoring data from the Scoring model

investigate the logs and results that are significant when running a Regression model

interpret the results from running a Classification model

interpret the results and logs form running a Clustering model

create an inference pipeline using a Python script

add a web service output to provide external access to the model

deploy the model as a predictive service

test the predictive service from an external app

summarize the key concepts covered in this course

Course Number:
it_clazaif_05_enus

Expertise Level
Beginner