DP-100: Designing and Implementing a Data Science Solution on Azure

Name: DP-100: Designing and Implementing a Data Science Solution on Azure
SKU: 108606
Price: 1990 EUR
Availability: InStock

Tämä kurssi opettaa kuinka koulutat omia koneoppimisen malleja omiin käyttötarkoituksiin, ja käytät niitä Azure pilvessä Azure Machine Learning palvelulla.

Kurssilla käydään läpi, kuinka tiedot valmistellaan koneoppimisen mallille, kuinka malli koulutetaan, arvioidaan ja julkaistaan käytettäväksi sekä kuinka mallin käyttöä monitoroidaan. Tämän kurssin jälkeen sinulla on hyvä käsitys koneoppimisen tekemisestä ja mahdollisuuksista Azuren pilvessä.

Tällä kurssilla keskitytään kouluttamaan malli alusta lähtien, eikä niinkään tukeuduta valmiisiin Microsoftin kouluttamiin malleihin, joiden käyttöä opiskellaan AI-102: Designing and Implementing a Microsoft Azure AI Solution -kurssilla.

Tuotetunnus (SKU): Ei saatavilla/-tietoa Osastot: Data Platform, Microsoft Azure, Tekoäly

Toteutus: Luokka, Online

Kesto: 4 päivää

Aloituspäivämäärät: Ask for details: sales@sulava.com

Materiaali: Microsoftin englanninkielinen materiaali (MOC)

Audience profileWhyPrerequisitesCourse content

Module 1: Design a data ingestion strategy for machine learning projects

Learn how to design a data ingestion solution for training data used in machine learning projects.

Learning objectives:

Identify your data source and format
Choose how to serve data to machine learning workflows
Design a data ingestion solution

Module 2: Design a machine learning model training solution

Learn how to design a model training solution for machine learning projects.

Learning objectives:

Identify machine learning tasks
Choose a service to train a model
Choose between compute options

Module 3: Design a model deployment solution

Learn how to design a model deployment solution and how the requirements of the deployed model can affect the way you train a model.

Learning objectives:

Understand how a model will be consumed.
Decide whether to deploy your model to a real-time or batch endpoint.

Module 4: Explore Azure Machine Learning workspace resources and assets

As a data scientist, you can use Azure Machine Learning to train and manage your machine learning models. Learn what Azure Machine Learning is, and get familiar with all its resources and assets.

Learning objectives:

Create an Azure Machine Learning workspace.
Identify resources and assets.
Train models in the workspace.

Module 5: Explore developer tools for workspace interaction

Learn how you can interact with the Azure Machine Learning workspace. You can use the Azure Machine Learning studio, the Python SDK (v2), or the Azure CLI (v2).

Learning objectives:

The Azure Machine Learning studio.
The Python Software Development Kit (SDK).
The Azure Command Line Interface (CLI).

Module 6: Make data available in Azure Machine Learning

Learn about how to connect to data from the Azure Machine Learning workspace. You’ll be introduced to datastores and data assets.

Learning objectives:

Work with Uniform Resource Identifiers (URIs).
Create and use datastores.
Create and use data assets.

Module 7: 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.

Learning objectives:

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

Module 8: Work with environments in Azure Machine Learning

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

Learning objectives:

Understand environments in Azure Machine Learning.
Explore and use curated environments.
Create and use custom environments.

Module 9: Find the best classification model with Automated Machine Learning

Learn how to find the best classification model with automated machine learning (AutoML). You’ll use the Python SDK (v2) to configure and run an AutoML job.

Learning objectives:

Prepare your data to use AutoML for classification.
Configure and run an AutoML experiment.
Evaluate and compare models.

Module 10: Track model training in Jupyter notebooks with MLflow

Learn how to use MLflow for model tracking when experimenting in notebooks.

Learning objectives:

Configure to use MLflow in notebooks
Use MLflow for model tracking in notebooks

Module 11: 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.

Learning objectives:

Convert a notebook to a script.
Test scripts in a terminal.
Run a script as a command job.
Use parameters in a command job.

Module 12: Track model training with MLflow in jobs

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

Learning objectives:

Use MLflow when you run a script as a job.
Review metrics, parameters, artifacts, and models from a run.

Module 13: Run pipelines in Azure Machine Learning

Learn how to create and use components to build pipeline in Azure Machine Learning. Run and schedule Azure Machine Learning pipelines to automate machine learning workflows.

Learning objectives:

Create components.
Build an Azure Machine Learning pipeline.
Run an Azure Machine Learning pipeline.

Module 14: Perform hyperparameter tuning with Azure Machine Learning

Learn how to perform hyperparameter tuning with a sweep job in Azure Machine Learning.

Learning objectives:

Define a hyperparameter search space.
Configure hyperparameter sampling.
Select an early-termination policy.
Run a sweep job.

Module 15: Deploy a model to a managed online endpoint

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

Learning objectives:

Use managed online endpoints.
Deploy your MLflow model to a managed online endpoint.
Deploy a custom model to a managed online endpoint.
Test online endpoints.

Module 16: Deploy a model to a batch endpoint

Learn how to deploy models to a batch endpoint. When you invoke a batch endpoint, you’ll trigger a batch scoring job.

Learning objectives:

Create a batch endpoint.
Deploy your MLflow model to a batch endpoint.
Deploy a custom model to a batch endpoint.
Invoke batch endpoints.

1990 € + ALV 25,5%

Toteutustapa

Kurssin aloituspäivä

Poista

Antti ”Kontti” Kontiainen

Consulting & Training

Antti on kokenut konsultti ja kouluttaja, joka on saanut paljon positiivista palautetta kyvystään esittää vaikeat tekniset asiat ymmärrettävästi ilman erikoista ammattisanastoa.

Lue lisää

DP-100: Designing and Implementing a Data Science Solution on Azure

Module 1: Design a data ingestion strategy for machine learning projects

Module 2: Design a machine learning model training solution

Module 3: Design a model deployment solution

Module 4: Explore Azure Machine Learning workspace resources and assets

Module 5: Explore developer tools for workspace interaction

Module 6: Make data available in Azure Machine Learning

Module 7: Work with compute targets in Azure Machine Learning

Module 8: Work with environments in Azure Machine Learning

Module 9: Find the best classification model with Automated Machine Learning

Module 10: Track model training in Jupyter notebooks with MLflow

Module 11: Run a training script as a command job in Azure Machine Learning

Module 12: Track model training with MLflow in jobs

Module 13: Run pipelines in Azure Machine Learning

Module 14: Perform hyperparameter tuning with Azure Machine Learning

Module 15: Deploy a model to a managed online endpoint

Module 16: Deploy a model to a batch endpoint

Tutustu myös

DP-300: Administering Microsoft Azure SQL Solutions

AZ-900: Microsoft Azure Fundamentals

AZ-204: Developing Solutions for Microsoft Azure