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Designing and Implementing a Data Science Solution on Azure

Codice corso: DP-100T01-A
Durata corso: 3gg

Introduzione

Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure.

This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud.

Prerequisiti

Before attending this course, students must have:

A fundamental knowledge of Microsoft Azure

Experience of writing Python code to work with data, using libraries such as Numpy, Pandas, and Matplotlib. 

Understanding of data science; including how to prepare data, and train machine learning models using common machine learning libraries such as Scikit-Learn, PyTorch, or Tensorflow.

Struttura del Corso

MODULE 1: Introduction to Azure Machine Learning

In this module, you will learn how to provision an Azure Machine Learning workspace and use it to manage machine learning assets such as data, compute, model training code, logged metrics, and trained models. You will learn how to use the web-based Azure Machine Learning studio interface as well as the Azure Machine Learning SDK and developer tools like Visual Studio Code and Jupyter Notebooks to work with the assets in your workspace.

Lessons

After completing this module, you will be able to:

Provision an Azure Machine Learning workspace

Use tools and code to work with Azure Machine Learning

Lab : Creating an Azure Machine Learning Workspace

Lab : Working with Azure Machine Learning Tools

MODULE 2: No-Code Machine Learning with Designer

This module introduces the Designer tool, a drag and drop interface for creating machine learning models without writing any code. You will learn how to create a training pipeline that encapsulates data preparation and model training, and then convert that training pipeline to an inference pipeline that can be used to predict values from new data, before finally deploying the inference pipeline as a service for client applications to consume.

Lessons

After completing this module, you will be able to:

Use designer to train a machine learning model

Deploy a Designer pipeline as a service

Lab : Creating a Training Pipeline with the Azure ML Designer

Lab : Deploying a Service with the Azure ML Designer

MODULE 3: Running Experiments and Training Models

In this module, you will get started with experiments that encapsulate data processing and model training code, and use them to train machine learning models.

Lessons

After completing this module, you will be able to:

Run code-based experiments in an Azure Machine Learning workspace

Train and register machine learning models

Lab : Running Experiments

Lab : Training and Registering Models

MODULE 4: Working with Data

Data is a fundamental element in any machine learning workload, so in this module, you will learn how to create and manage datastores and datasets in an Azure Machine Learning workspace, and how to use them in model training experiments.

Lessons

After completing this module, you will be able to:

Create and consume datastores

Create and consume datasets

Lab : Working with Datastores

Lab : Working with Datasets

MODULE 5: Compute Contexts

In this module, you'll learn how to manage experiment environments that ensure consistent runtime consistency for experiments, and how to create and use compute targets for experiment runs.

Lessons

After completing this module, you will be able to:

Create and use Environments

Create and use Compute Targets

Lab : Working with Environments

Lab : Working with Compute Targets

MODULE 6: Orchestrating Operations with Pipelines

Now that you understand the basics of running workloads as experiments that leverage data assets and compute resources, it's time to learn how to orchestrate these workloads as pipelines of connected steps. Pipelines are key to implementing an effective Machine Learning Operationalization (ML Ops) solution in Azure, so you'll explore how to define and run them in this module.

Lessons

After completing this module, you will be able to:

Create Pipelines to automate machine learning workflows

Publish and run Pipelines services

Lab : Creating a Pipeline

Lab : Publishing a Pipeline

MODULE 7: Deploying and Consuming Models

In this module learn how to deploy models for real-time inferencing, and for batch inferencing.

Lessons

After completing this module, you will be able to:

Publish a model as a Real-time Inferenc service

Publish a model as a Batch inference service

Lab : Creating a Real-time Inferencing Service

Lab : Creating a Batch Inferencing Service

MODULE 8: Training Optimal Models

In this module, you'll explore how you can use hyperparameter tuning and automated machine learning to take advantage of cloud-scale compute and find the best model for your data.

Lessons

After completing this module, you will be able to:

Optimize hyperparameters for model training

Use automated machine learning to find the optimal model for your data

Lab : Tuning Hyperparameters

Lab : Using Automated Machine Learning

MODULE 9: Interpreting Models

This module describes how you can interpret models to explain how feature importance determines their predictions.

Lessons

After completing this module, you will be able to:;

Generate model explanations with automated machine learning

Use explainers to interpret machine learning models

Lab : Reviewing Automated Machine Learning Explanations

Lab : Interpreting Models

MODULE 10: Monitoring Models

This module describes techniques for monitoring models and their data.

Lessons

After completing this module, you will be able to:

Use Application Insights to monitor a published model

Monitor Data Drift

Lab : Monitoring a Model with Application Insights

Lab : Monitoring Data Drift

P.IVA 06249920965
C.C.I.A.A. REA: MI - 1880014
Cap. Soc. € 12.000,00

Contatti

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