Machine Learning on Google Cloud

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Duración del curso
32 horas
Lugar de impartición
Madrid / Online
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Modalidad
Aula Virtual, Presencial
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Fecha de inicio
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Acerca del curso

This course teaches you how to build Vertex AI AutoML models without writing a single line of code; build BigQuery ML models knowing basic SQL; create Vertex AI custom training jobs you deploy using containers (with little knowledge of Docker0; use Feature Store for data management and governance; use feature engineering for model improvement;determine the appropriate data preprocessing options for your use case; write distributed ML models that scale in TensorFlow; and leverage best practices to implement machine learning on Google Cloud. Learn all this and more!

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  • Build, train, and deploy a machine learning model without writing
    a single line of code using Vertex AI AutoML
  • Understand when to use AutoML and Big Query ML
  • Create Vertex AI managed datasets
  • Add features to a Feature Store
  • Describe Analytics Hub, Dataplex, and Data Catalog
  • Describe hyperparameter tuning using Vertex Vizier and how it can
    be used to improve model performance.
  •  Create a Vertex AI Workbench User-Managed Notebook, build
    a custom training job, and then deploy it using a Docker container
  • Describe batch and online predictions and model monitoring
  • Describe how to improve data quality
  • Perform exploratory data analysis
  • Build and train supervised learning models
  • Optimize and evaluate models using loss functions and
    performance metrics
  • Create repeatable and scalable train, eval, and test datasets
  • Implement ML models using TensorFlow/Keras
  • Describe how to represent and transform features
  • Understand the benefits of using feature engineering
  • Explain Vertex AI Pipelines

  • Some familiarity with basic machine learning concepts
  • Basic proficiency with a scripting language, preferably Python

Documentación Oficial de Google Cloud - Machine Learning on Google Cloud

Curso 1: How Google Does Machine Learning

Objectives:

    Curso 3: TensorFlow on Google Cloud

    Objectives:

    • Create TensorFlow and Keras machine learning models.
    • Describe TensorFlow key components.
    • Use the tf.data library to manipulate data and large datasets.
    •  Build a ML model using tf.keras preprocessing layers.
    • Use the Keras Sequential and Functional APIs for simple and advanced model
      creation. Understand how model subclassing can be used for more
      customized models.
    • Use tf.keras.preprocessing utilities for working with image data, text data, and
      sequence data.
    •  Train, deploy, and productionalize ML models at scale with Cloud AI Platform.

    Activities:

    • Hands-On Labs
    • Module Quizzes
    • Module Readings
  • Describe the Vertex AI Platform and how it is used to quickly build, train, and deploy

    AutoML machine learning models without writing a single line of code

  • Describe best practices for implementing machine learning on Google Cloud
  •  Develop a data strategy around machine learning
  • Examine use cases that are then reimagined through an ML lens
  • Leverage Google Cloud Platform tools and environment to do ML
  • Learn from Google's experience to avoid common pitfalls
  • Carry out data science tasks in online collaborative notebooks

Activities:

  • Hands-On Labs
  • Module Quizzes
  • Module Readings

Curso 2: Launching into Machine Learning

Objectives:

  • Describe Vertex AI AutoML and how to build, train, and deploy an ML model without
    writing a single line of code.
  • Describe Big Query ML and its benefits.
  • Describe how to improve data quality.
  • Perform exploratory data analysis.
  • Build and train supervised learning models.
  • Optimize and evaluate models using loss functions and performance metrics.
  • Mitigate common problems that arise in machine learning.
  • Create repeatable and scalable training, evaluation, and test datasets.

Activities:

  • Hands-On Labs
  • Module Quizzes
  • Module Readings

Curso 3: TensorFlow on Google Cloud

Objectives:

  • Create TensorFlow and Keras machine learning models.
  • Describe TensorFlow key components.
  • Use the tf.data library to manipulate data and large datasets.
  •  Build a ML model using tf.keras preprocessing layers.
  • Use the Keras Sequential and Functional APIs for simple and advanced model
    creation. Understand how model subclassing can be used for more
    customized models.
  • Use tf.keras.preprocessing utilities for working with image data, text data, and
    sequence data.
  •  Train, deploy, and productionalize ML models at scale with Cloud AI Platform.

Activities:

  • Hands-On Labs
  • Module Quizzes
  • Module Readings

Curso 4: Feature Engineering

Objectives:

  • Describe Vertex AI Feature Store
  • Compare the key required aspects of a good feature
  • Combine and create new feature combinations through feature crosses
  • Perform feature engineering using BigQuery ML, Keras, and TensorFlow
  • Understand how to preprocess and explore features with Dataflow and Dataprep by Trifacta
  • Understand and apply how TensorFlow transforms features

Activities:

  • Hands-On Labs
  • Module Quizzes
  • Module Readings

 

Curso 5: Machine Learning in the Enterprise

Objectives:

  • Understand the tools required for data management and governance.
  • Describe the best approach for data preprocessing: from providing an overview of Dataflow and Dataprep to using SQL for preprocessing tasks
  • Explain how AutoML, BigQuery ML, and custom training differ and when to use a particular framework
  • Describe hyperparameter tuning using Vertex Vizier and how it can be used
    to improve model performance
  • Explain prediction and model monitoring and how Vertex AI can be used to manage ML models
  •  Describe the benefits of Vertex AI Pipelines

Activities:

  • Hands-On Labs
  • Module Quizzes
  • Module Readings

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