Microsoft - Designing and Implementing a Data Science Solution on Azure

Dauer

Dauer:

Nur 2 Tage

Methode

Methode:

Klassenraum / Online / Hybrid

nächster Termin

nächster Termin:

24.6.2024 (Montag)

Overview

On this accelerated Designing and Implementing a Data Science Solution on Azure course, you will 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 teaches you how to create end-to-end solutions in Microsoft Azure where you will learn how to manage Azure resources for machine learning; run experiments and train models; deploy and operationalize machine learning solutions; and implement responsible machine learning.

You will also learn to use Azure Databricks to explore, prepare, and model data; and integrate Databricks machine learning processes with Azure Machine Learning. This certification is an opportunity to prove knowledge and expertise 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.

By the end of this program, you will be ready to take the DP-100: Designing and Implementing a Data Science Solution on Azure.

At the end of this course, you’ll achieve your Designing and Implementing a Data Science Solution on Azure certification.

Through Firebrand’s Lecture | Lab | Review methodology you’ll certify at twice the speed of traditional training and get access to courseware, learn from certified instructors, and train in a distraction-free environment.

40% faster

Distraction-free environment

Audience

This course is ideal 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.

Benefits

In einem Firebrand Intensiv-Training profitieren Sie von folgenden Vorteilen:

  • Zwei Optionen - Präsenz- oder Onlinetraining
  • Ablenkungsfreie Lernumgebung
  • Eigene Trainings- und Prüfungszentren (Pearson VUE Select Partner)
  • Effektives Training mit praktischen Übungseinheiten und intensiver Betreuung durch unsere Trainer
  • Umfassendes Leistungspaket mit allem, was Sie benötigen, um Ihre Zertifizierung zu erhalten, inklusive unserer Firebrand Leistungsgarantie.

Curriculum

Designing and Implementing a Data Science Solution on Azure

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.

  • Getting Started with Azure Machine Learning
  • Azure Machine Learning Tools
  • Lab : Creating an Azure Machine Learning Workspace
  • Lab : Working with Azure Machine Learning Tools

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
  • 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.

    • Training Models with Designer
    • Publishing Models with Designer
    • Lab : Creating a Training Pipeline with the Azure ML Designer
    • Lab : Deploying a Service with the Azure ML Designer

    After completing this module, you will be able to;

    • Use designer to train a machine learning model
    • Deploy a Designer pipeline as a service

    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.

    • Introduction to Experiments
    • Training and Registering Models
    • Lab : Running Experiments
    • Lab : Training and Registering Models

    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

    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.

    • Working with Datastores
    • Working with Datasets
    • Lab : Working with Datastores
    • Lab : Working with Datasets

    After completing this module, you will be able to:

    • Create and consume datastores
    • Create and consume datasets

    Module 5: Compute Contexts

    One of the key benefits of the cloud is the ability to leverage compute resources on demand, and use them to scale machine learning processes to an extent that would be infeasible on your own hardware. 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.

    • Working with Environments
    • Working with Compute Targets
    • Lab : Working with Environments
    • Lab : Working with Compute Targets

    After completing this module, you will be able to:

    • Create and use environments
    • Create and use 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.

    • Introduction to Pipelines
    • Publishing and Running Pipelines
    • Lab : Creating a Pipeline
    • Lab : Publishing a Pipeline

    After completing this module, you will be able to:

    • Create pipelines to automate machine learning workflows
    • Publish and run pipeline services

    Module 7: Deploying and Consuming Models

    Models are designed to help decision making through predictions, so they're only useful when deployed and available for an application to consume. In this module learn how to deploy models for real-time inferencing, and for batch inferencing.

    • Real-time Inferencing
    • Batch Inferencing
    • Lab : Creating a Real-time Inferencing Service
    • Lab : Creating a Batch Inferencing Service

    After completing this module, you will be able to:

    • Publish a model as a real-time inference service
    • Publish a model as a batch inference service

    Module 8: Training Optimal Models

    By this stage of the course, you've learned the end-to-end process for training, deploying, and consuming machine learning models; but how do you ensure your model produces the best predictive outputs for your data? 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.

    • Hyperparameter Tuning
    • Automated Machine Learning
    • Lab : Tuning Hyperparameters
    • Lab : Using Automated Machine Learning

    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

    Module 9: Interpreting Models

    Many of the decisions made by organizations and automated systems today are based on predictions made by machine learning models. It's increasingly important to be able to understand the factors that influence the predictions made by a model, and to be able to determine any unintended biases in the model's behaviour. This module describes how you can interpret models to explain how feature importance determines their predictions.

    • Introduction to Model Interpretation using Model Explainers
    • Lab : Reviewing Automated Machine Learning Explanations
    • Lab : Interpreting Models

    After completing this module, you will be able to:

    • Generate model explanations with automated machine learning
    • Use explainers to interpret machine learning models

    Module 10: Monitoring Models

    After a model has been deployed, it's important to understand how the model is being used in production, and to detect any degradation in its effectiveness due to data drift. This module describes techniques for monitoring models and their data.

    • Monitoring Models with Application Insights
    • Monitoring Data Drift
    • Lab : Monitoring a Model with Application Insights
    • Lab : Monitoring Data Drift

    After completing this module, you will be able to:

    • Use Application Insights to monitor a published model
    • Monitor data drift

Exam Track

At the end of this accelerated course, you’ll sit the following exam at the Firebrand Training centre, covered by your Certification Guarantee:

Designing and Implementing a Data Science Solution on Azure

  • Exam code: DP-100: Designing and Implementing a Data Science Solution on Azure
  • Format: This exam measures your ability to accomplish the following technical tasks: manage Azure resources for machine learning; run experiments and train models; deploy and operationalize machine learning solutions; and implement responsible machine learning.
  • Passing score: 700
  • Domains: -
  • Manage Azure resources for machine learning (25—30%)
  • Run experiments and train models (20—25%)
  • Deploy and operationalize machine learning solutions (35—40%)
  • Implement responsible machine learning (5—10%)

What's Included

Prerequisites

Before attending this accelerated course, you should have subject matter expertise applying data science and machine learning to implement and run machine learning workloads on Microsoft Azure. You should also have knowledge and experience in data science and using Azure Machine Learning and Azure Databricks.

Before attending this course, you should 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.

Sind Sie sich unsicher, ob Sie die Voraussetzungen erfüllen? Wir besprechen gerne mit Ihnen Ihren technischen Hintergrund, Erfahrung und Qualifikation, um herauszufinden, ob dieser Intensivkurs der richtige für Sie ist.

Erfahrungsberichte

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