Azure AI Fundamentals: Evaluating Models with the ML Designer
Azure 2020
| Beginner
- 16 Videos | 2h 2m 34s
- Includes Assessment
- Earns a Badge
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.
WHAT YOU WILL LEARN
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discover the key concepts covered in this courseadd a Scoring model component in the ML Designerdescribe model evaluation types like MAE and R2use an evaluator on a model and interpret the metricsrun and monitor a complete pipelineanalyze the evaluation results in the output and logs section in the ML Designeridentify and investigate the details of the evaluation resultsvisualize the scoring data from the Scoring model
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investigate the logs and results that are significant when running a Regression modelinterpret the results from running a Classification modelinterpret the results and logs form running a Clustering modelcreate an inference pipeline using a Python scriptadd a web service output to provide external access to the modeldeploy the model as a predictive servicetest the predictive service from an external appsummarize the key concepts covered in this course
IN THIS COURSE
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1.Course Overview1m 27sUP NEXT
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2.Adding and Using Scoring on Models7m 32s
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3.Model Evaluation Types7m 14s
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4.Using Evaluators on the Model6m 10s
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5.Running a Pipeline10m 44s
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6.Analyzing the Evaluation Results Output and Logs8m 32s
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7.Exploring the Evaluation Results Details9m 24s
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8.Visualizing the Data in the Scoring Model9m 2s
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9.Investigating Results from a Regression Model10m 17s
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10.Interpreting Results from a Classification Model10m 22s
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11.Investigating Results from a Clustering Model10m 50s
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12.Creating an Inference Pipeline10m 56s
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13.Adding a Web Service Output6m 59s
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14.Deploying a Predictive Service6m 29s
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15.Testing a Predictive Service5m 41s
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16.Course Summary55s
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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