Diseño e implementación de una solución de ciencia de datos en Azure (DP 100T01)

Duración: 21 horas.
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A quien va dirigido

Este curso está diseñado para científicos de datos con conocimientos existentes de Python y marcos de aprendizaje automático como Scikit-Learn, PyTorch y Tensorflow, que desean crear y operar soluciones de aprendizaje automático en la nube.

Descripción

Aprenda a operar soluciones de aprendizaje automático a escala de la nube 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, el entrenamiento y la implementación de modelos y el monitoreo de la solución de aprendizaje automático en Microsoft Azure.

A quien va dirigido

Este curso está diseñado para científicos de datos con conocimientos existentes de Python y marcos de aprendizaje automático como Scikit-Learn, PyTorch y Tensorflow, que desean crear y operar soluciones de aprendizaje automático en la nube.

Descripción

Aprenda a operar soluciones de aprendizaje automático a escala de la nube 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, el entrenamiento y la implementación de modelos y el monitoreo de la solución de aprendizaje automático en Microsoft Azure.

Los científicos de datos de Azure exitosos comienzan este rol con un conocimiento fundamental de los conceptos de computación en la nube y experiencia en ciencia de datos general y herramientas y técnicas de aprendizaje automático.

Específicamente:

  • Creación de recursos en la nube en Microsoft Azure.
  • Usar Python para explorar y visualizar datos.
  • Entrenamiento y validación de modelos de aprendizaje automático mediante marcos comunes como Scikit-Learn, PyTorch y TensorFlow.

Para adquirir estos requisitos previos, realice la siguiente formación gratuita en línea antes de asistir al curso:

Si es completamente nuevo en la ciencia de datos y el aprendizaje automático, primero complete Microsoft Azure AI Fundamentals .

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
  • Provision an Azure Machine Learning workspace
  • Use tools and code to work with Azure Machine Learning

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

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
  • Run code-based experiments in an Azure Machine Learning workspace
  • Train and register machine learning models

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
  • Create and consume datastores
  • Create and consume datasets

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


  • Create and use environments
  • Create and use compute targets

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

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


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

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
  • Optimize hyperparameters for model training
  • Use automated machine learning to find the optimal model for your data

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.

  • Introduction to Model Interpretation
  • using Model Explainers


  • Generate model explanations with automated machine learning
  • Use explainers to interpret machine learning 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
  • Use Application Insights to monitor a published model
  • Monitor data drift