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Designing and Implementing a Data Science Solution on Azure


Designing and Implementing a Data Science Solution on Azure

In this course the students 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. 


Objetivos

At the end of the course the students will be able to: 

  • Define and prepare the development environment 
  • Prepare data for modeling 
  • Perform feature engineering 
  • Develop models 

Perfil de los alumnos

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. 


Requisitos previos

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. 

Profesorado

Our team of highly qualified instructors combine training activities with the development of their profession as experts in the field of IT. Professionals certified by the major manufacturers capable of transferring an enjoyable and easy to understand technical concepts more abstract. 


Documentación

A copy of the official Microsoft documentation. 


Contenidos del Designing and Implementing a Data Science Solution on Azure

1. Introduction to Azure Machine Learning 

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

2. No-Code Machine Learning with Designer 

  • 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 

3. Running Experiments and Training Models 

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

4. Working with Data 

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

5. Compute Contexts 

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

6. Orchestrating Operations with Pipelines 

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

7. Deploying and Consuming Models 

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

8. Training Optimal Models 

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

9. Interpreting Models 

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

10. Monitoring Models 

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

Metodología

Active and participatory course through demonstrations, practical exercises and clinical analysis of users of all the theoretical topics taught by the instructor in order to deal with real cases of the related product. The trainer will also use different dynamics that allow group work in the classroom as challenges, evaluation exams and real cases to prepare for the associated Microsoft certification exam, if there is one. 


Certificaciones

Continual evaluation based on group and individual activities. The faculty will give continuous feedback and at the end of the activities to each participant 

During the course the participants will complete an evaluation test that must be passed with more than 75%. They will have one hour available for its realization 

 

Conditions of additional certification services are subject to the terms of the license owner or of the authorized certification authority. 


Acreditación

Se emitirá Certificado de Asistencia sólo a los alumnos con una asistencia superior al 75% y Diploma aprovechamiento si superan también la prueba de evaluación.


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