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DP-100 Microsoft Azure Data Scientist (DS) Exam Preparation
Introduction
Introduction to Course (2:11)
Exam Requirements (6:56)
Create an Azure Machine Learning Workspace
Create an Azure Machine Learning Workspace (7:27)
Azure ML Workspace Settings - Portal (5:10)
Azure ML Studio Settings (6:03)
Azure Learning Workspace
Data Stores and Datasets (10:10)
Create Additional Datasets (9:19)
Manage Experiment Commute Context
Create an Experiment Compute Instance (6:35)
Manage Multiple Compute Instances (5:38)
Create Compute Targets and Clusters (6:46)
Using Azure Machine Learning
Creating our First ML Pipeline (9:28)
Submitting Pipeline (8:31)
Custom Code in Pipeline (4:33)
Understanding Complicated Pipeline (10:49)
Evaluating Execution Results (6:13)
Errors in Azure ML Designer (3:30)
Various Modules of Azure ML Designer (10:18)
Azure Machine Learning Experience
Setup SDK (8:38)
Create ML Workspace using SDK (8:01)
Simple Program in Python (14:27)
Run Training in an Azure Machine Learning
Train Model using SDK (10:43)
Submit Experiment using SDK (5:00)
Automate the Model Training Process
Create a Pipeline by using SDK (11:03)
Automate ML to create optimal models
AutoML Overview (11:24)
AutoML with SDK (2:47)
Use Hyper drive to Tune Hyper parameters
Understanding what is Hyperdrive (7:51)
Manage Models
Register a Trained Model (4:57)
Create Production Compute Target
Create Production Compute Targets (7:43)
Deploy Model as Service
Deploy AutoML (11:42)
Create an AutoML Endpoint (3:32)
Deploy ML Designer for Real Time (4:52)
Deploy SDK Models (5:21)
Create Pipeline for Batch Inferencing
Publish a Pipeline for Batch Inference (3:25)
Conclusion
Conclusion (1:17)
Teach online with
Create Production Compute Targets
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