Learnit Training
Course Designing and Implementing a Data Science Solution on Azure
  • Official Microsoft teaching materials
  • E-learning - study when you want to
  • 6 months access to the material
  • Preparing for the Microsoft certification exams
  • Practical training

This e-learning, with official Microsoft material, is a preparation for the official Microsoft exam to certify you for this subject. The training is fully in English, therefore part of the information page is also in English. On the "Details" tab you will find a short description and then all the topics that are covered in this e-learning.

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Course Designing and Implementing a Data Science Solution on Azure

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

Contents

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 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

  • Getting Started with Azure Machine Learning
  • Azure Machine Learning Tools
  • 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

  • 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

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

  • Introduction to Experiments
  • Training and Registering 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

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

Lessons

  • Working with Environments
  • Working with 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

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

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.

Lessons

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

Lessons

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

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

Lessons

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

Lessons

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

Training material

To follow this training you will receive official Microsoft study and exercise material. You You will receive the e-book that serves as a reference work for the practical execution of assignments in an an online learning environment. You will have access to this learning environment for six months. software, only a computer with an internet connection. In the online labs, you work through scenarios where you can immediately apply what you have learned in practice. By means of a practice exam, you can prepare yourself well for the corresponding Microsoft exam. This practice exam is also online. It consists of questions similar to those you can expect on the actual exam and provides detailed explanations of both correct and incorrect and incorrect answers. The training course is in English and can be completed in approximately 3 days. The training course is fully English-based and can be completed in approximately 3 days.

Certificate

After completing the training you will receive a certificate from Learnit. If you also book and complete the DP-100 exam, you will also receive an official certificate from Microsoft.

Prior knowledge

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. Azure Fundamentals (AZ-900) is not required.

Note

During this e-learning you get 6 months access to the online learning environment in which you can apply the material to practice. If you follow the training to certify yourself, you can also directly book exams and follow-up and follow-up training courses. If you combine training courses, you will benefit from a bundle discount. for more information on our Microsoft certification page. The cost of an exam is €175 euro (excl. VAT). You can schedule the exam online and follow it at a time that suits you. During the exam, you will be monitored by a proctor via webcam and microphone. If you want to you can indicate this in the comments field on the registration form.

All advantages at a glance:

  • 6 months access to the learning environment
  • A practice test
  • Preparation for Microsoft Exam DP-100
  • Part of the Designing and Implementing a Data Science Solution on Azure certification
  • The official Microsoft study material
  • Possibility to book exams and follow-up trainings as well

Investment

E-learning, per person:


Price (excl. VAT)

€ 400,-

VAT 21%

€ 84,-

Total incl. VAT

€ 484,-