This four day course teaches the participant how to maximize Machine Learning solutions for the cloud using Azure. This class also explores the attendee's existing knowledge of Python and Machine Learning to manage data ingestion and preparation, model training and deployment.

4 days - $2,100.00

Course taught by an expert Computer Coding Instructor.

Prerequisites:

Basic knowledge of Python is required.

Course Outline

Getting Started with Azure Machine Learning
Introduction to Azure Machine Learning
Provision an Azure Machine Learning workspace
Use tools and code to work with Azure Machine Learning

Visual Tools for Machine Learning
Automated Machine Learning
Azure Machine Learning Designer
Use automated Machine Learning to train a Machine Learning model
Use Azure Machine Learning Designer to train a model

Running Experiments and Training Models
Introduction to Experiments
Training and Registering Models
Running code-based experiments in an Azure Machine Learning workspace
Train and register Machine Learning models

Working with Data
Working with Datastores
Working with Datasets
Create and use Datastores
Create and use Datasets

Working with Compute
Working with Environments
Working with Compute Targets
Create and use Environments
Create and use compute Targets

Orchestrating Operations with Pipelines
Introduction to Pipelines
Publishing and Running Pipelines
Create Pipelines to automate Machine Learning workflows
Publish and run Pipeline services

Deploying and Consuming Models
Real-time Inferencing
Batch Inferencing
Continuous Integration and Delivery
Publish a Model as a real-time inference service
Publish a Model as a batch inference service
Describe techniques to implement continuous integration and delivery

Training Optimal Models
Hyperparameter Tuning
Automated Machine Learning
Optimize hyperparameters for Model Training
Use automated Machine Learning to find the optimal model for the data

Responsible Machine Learning
Differential Privacy
Model Interpretability
Fairness
Apply differential privacy to Data Analysis
Use explainers to interpret Machine Learning Models
Evaluate Models for fairness

Monitoring Models
Monitoring Models with Application Insights
Monitoring Data Drift
Use Application Insights to Monitor a Published Model
Monitor data drift