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Train and deploy a machine learning model with Azure Machine Learning (DP-3007)

Train and deploy a machine learning model with Azure Machine Learning (DP-3007)

Course 8697

Duration: 1 day
Language: English
Level: Intermediate

Azure Machine Learning DP-3007 training course Delivery Methods

In-Person
Online
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Azure Machine Learning DP-3007 training course Information

Upon successful completion of this course, students will master essential skills to:

Make data available in Azure Machine Learning.
Work with compute targets in Azure Machine Learning.
Run a training script as a command job in Azure Machine Learning.
Track model training with MLflow in jobs.
Register an MLflow model in Azure Machine Learning.
Deploy a model to a managed online endpoint.

Training Prerequisites

To maximise the benefits of this course, participants should have familiarity with the data science process. While the course doesn't delve deeply into data science concepts, a basic understanding is recommended. Additionally, familiarity with Python is essential, as the course focuses on utilising the Python SDK for interacting with Azure Machine Learning.

Azure Machine Learning DP-3007 training course Outline

Module 1: Make Data Available in Azure Machine Learning

Introduction
Understand URIs
Create a datastore
Create a data asset

Exercise: Make data available in Azure Machine Learning

Module 2: Work with Compute Targets in Azure Machine Learning

Introduction
Choose the appropriate compute target
Create and use a compute instance
Create and use a compute cluster

Exercise: Work with compute resources

Module 3: Work with Environments in Azure Machine Learning

Introduction
Understand environments
Explore and use curated environments
Create and use custom environments

Exercise: Work with environments

Module 4: Run a Training Script as a Command Job in Azure Machine Learning

Introduction
Convert a notebook to a script
Run a script as a command job
Use parameters in a command job

Exercise: Run a training script as a command job

Module 5: Track Model Training with MLflow in Jobs

Introduction
Track metrics with MLflow
View metrics and evaluate models

Exercise: Use MLflow to track training jobs

Module 6: Register an MLflow Model in Azure Machine Learning

Introduction
Log models with MLflow
Understand the MLflow model format
Register an MLflow model

Exercise: Log and register models with MLflow

Module 7: Deploy a Model to a Managed Online Endpoint

Introduction
Explore managed online endpoints
Deploy your MLflow model to a managed online endpoint
Deploy a model to a managed online endpoint
Test managed online endpoints

Exercise: Deploy an MLflow model to an online endpoint

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Hands-On Labs
Microsoft Official Content
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Nov 26 9:00 AM - 4:30 PM GMT

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Apr 24 2:00 PM - 9:30 PM BST

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Sep 9 2:00 PM - 9:30 PM BST

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Train and deploy a machine learning model with Azure Machine Learning (DP-3007)

Azure Machine Learning DP-3007 training course Delivery Methods

Azure Machine Learning DP-3007 training course Information

Azure Machine Learning DP-3007 training course Outline

Module 1: Make Data Available in Azure Machine Learning

Module 2: Work with Compute Targets in Azure Machine Learning

Module 3: Work with Environments in Azure Machine Learning

Module 4: Run a Training Script as a Command Job in Azure Machine Learning

Module 5: Track Model Training with MLflow in Jobs

Module 6: Register an MLflow Model in Azure Machine Learning

Module 7: Deploy a Model to a Managed Online Endpoint

Upgrade your course experience with: Subscriptions

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Upgrade your course experience with: Subscriptions

Get the Advantage £3,150

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