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To train a machine learning model with Azure Machine Learning, you need to make data available and configure the necessary compute. After training your model and tracking model metrics with MLflow, you can decide to deploy your model to an online endpoint for real-time predictions. Throughout this learning path, you explore how to set up your Azure Machine Learning workspace, after which you train and deploy a machine learning model.

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Module 1: Make data available in Azure Machine Learning

Learn about how to connect to data from the Azure Machine Learning workspace. You're introduced to datastores and data assets.

Introduction
Understand URIs
Create a datastore
Create a data asset
Exercise - Make data available in Azure Machine Learning
Knowledge check
Summary

Module 2: Work with compute targets in Azure Machine Learning

Learn how to work with compute targets in Azure Machine Learning. Compute targets allow you to run your machine learning workloads. Explore how and when you can use a compute instance or compute cluster.

Introduction
Choose the appropriate compute target
Create and use a compute instance
Create and use a compute cluster
Exercise - Work with compute resources
Knowledge check
Summary

Module 3: Work with environments in Azure Machine Learning

Learn how to use environments in Azure Machine Learning to run scripts on any compute target.

Introduction
Understand environments
Explore and use curated environments
Create and use custom environments
Exercise - Work with environments
Knowledge check
Summary

Module 4: Run a training script as a command job in Azure Machine Learning

Learn how to convert your code to a script and run it as a command job in Azure Machine Learning.

Introduction
Convert a notebook to a script
Run a script as a command job5
Use parameters in a command job
Exercise - Run a training script as a command job
Knowledge check
Summary

Module 5: Track model training with MLflow in jobs

Learn how to track model training with MLflow in jobs when running scripts.

Introduction
Track metrics with MLflow
View metrics and evaluate models
Exercise - Use MLflow to track training jobs
Knowledge check
Summary

Module 6: Register an MLflow model in Azure Machine Learning

Learn how to log and 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
Knowledge check
Summary

Module 7: Deploy a model to a managed online endpoint

Learn how to deploy models to a managed online endpoint for real-time inferencing.

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
Knowledge check
Summary

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Train and Deploy a Machine Learning Model with Azure Machine Learning - Applied Skills Workshop Training Course in Finland

Finland is a country located in northern Europe. Helsinki is the capital and largest city of the country. The majority of the people are Finns but there is also a small Lapp population in Lapland, where the country is famous for the Northern Lights. Finland's national languages are Finnish and Swedish.

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Finland has a strong educational system, and is widely regarded as one of the world's most literate countries. In fact, Finland's literacy rate is one of the highest in the world, and its students consistently perform well in international tests of math and reading ability.

Also, as a pioneer in environmental sustainability, Finland is known for its efforts to reduce its carbon footprint and promote clean energy. This Nordic country is also famous for its unique and distinctive cultural heritage, including its traditional folk music and its elaborate traditional costumes.

Helsinki, Finland's capital city, is the country's business center. Helsinki is Finland's largest city, and it is home to many of the country's major corporations and organizations, including many of the country's leading technology firms. The city is also a commercial, trade, and financial center, as well as one of the busiest ports in the Nordic region.

Take advantage of our diverse IT course offerings, spanning programming, software development, business skills, data science, cybersecurity, cloud computing and virtualization. Our knowledgeable instructors will provide you with practical training and industry insights, delivered directly to your chosen venue in Finland.

Train and Deploy a Machine Learning Model with Azure Machine Learning - Applied Skills Workshop Training in Finland

Upcoming Trainings

Train and Deploy a Machine Learning Model with Azure Machine Learning - Applied Skills Workshop Training in Finland

Prerequisites

Outline

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