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

  

 

Duración: 15 horas lectivas

Detalles

 

 

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.

Objetivos

Aprenda a operar soluciones de aprendizaje automático con Azure Machine Learning. Este curso le enseña a aprovechar su conocimiento existente de Python y el aprendizaje automático para administrar la ingestión y preparación de datos, la capacitación e implementación de modelos, y el monitoreo de soluciones de aprendizaje automático en Microsoft Azure.

Ubicación

Presencial en Madrid y Barcelona.
Disponibles también en Online Direct

Contacto:

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Temario

Module 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

Module 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

Module 3: Running Experiments and Training Models

  • Introduction to Experiments
  • Training and Registering Models

Lab : Running Experiments

Lab : Training and Registering Models

Module 4: Working with Data

  • Working with Datastores
  • Working with Datasets

Lab : Working with Datastores

Lab : Working with Datasets

Module 5: Compute Contexts

  • Working with Environments
  • Working with Compute Targets

Lab : Working with Environments

Lab : Working with Compute Targets

Module 6: Orchestrating Operations with Pipelines

  • Introduction to Pipelines
  • Publishing and Running Pipelines

Lab : Creating a Pipeline

Lab : Publishing a Pipeline

Module 7: Deploying and Consuming Models

  • Real-time Inferencing
  • Batch Inferencing

Lab : Creating a Real-time Inferencing Service

Lab : Creating a Batch Inferencing Service

Module 8: Training Optimal Models

  • Hyperparameter Tuning
  • Automated Machine Learning

Lab : Tuning Hyperparameters

Lab : Using Automated Machine Learning

Module 9: Interpreting Models

  • Introduction to Model Interpretation
  • using Model Explainers

Lab : Reviewing Automated Machine Learning Explanations

Lab : Interpreting Models

Module 10: Monitoring Models

  • Monitoring Models with Application Insights
  • Monitoring Data Drift

 

 

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