Curso ML Pipelines on Google Cloud

 

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Acerca del curso

En el curso ML Pipelines on Google Cloud aprenderás sobre TensorFlow Extended (TFX), que es la plataforma de machine learning de producción de Google basada en TensorFlow para la gestión de pipelines y metadatos de ML. Los primeros módulos analizan los componentes de pipeline, la orquestación de pipeline con TFX, cómo puede automatizarse el pipeline a través de CI/CD y cómo administrar los metadatos de ML. Luego, se analiza cómo automatizar y reutilizar pipelines de ML en múltiples marcos de ML, como tensorflow, pytorch, scikit learn y xgboost. También, aprenderás a usar Cloud Composer para orquestar tus pipelines de capacitación continua y MLflow para administrar el ciclo de vida completo del machine learning.

  • Científicos de datos que busquen generar un impacto comercial mediante la conversión rápida del prototipo de machine learning a la producción.
  • Ingenieros de software que busquen desarrollar habilidades de ingeniería de machine learning.
  • Ingenieros de machine learning que deseen adoptar Google Cloud.

  • Organizar el entrenamiento y la implementación de modelos con TFX y Cloud AI Platform.
  • Operar modelos de machine learning implementados de manera efectiva y eficiente.
  • Realizar una capacitación continua con varios marcos (Scikit Learn, XGBoost, PyTorch) y organizar pipelines con Cloud Composer y MLFlow.
  • Integre los flujos de trabajo de machine learning con los flujos de trabajo de gestión de datos ascendentes y descendentes para mantener la gestión integral de linaje y metadatos.

Módulo 1: Introduction to TFX

  • Develop a high level understanding of TFX standard pipeline components.
  • Learn how to use a TFX Interactive Context for prototype development of TFX pipelines.
  • Work with the Tensorflow Data Validation (TFDV) library to check and analyze input data.
  • Utilize the Tensorflow Transform (TFT) library for scalable data preprocessing and feature transformations.
  • Use the KerasTuner library for model hyperparameter tuning.
  • Employ the Tensorflow Model Analysis (TFMA) library for model evaluation.

Módulo 2: Pipeline orchestration with TFX

Use the TFX CLI and Kubeflow UI to build and deploy TFX pipelines to a hosted AI Platform Pipelines instance on Google Cloud.

  • Deploy a TensorFlow model trained using AI Platform Training to AI Platform Prediction.
  • Perform advanced distributed hyperparameter tuning using CloudTuner and Cloud AI Platform Vizier.

Módulo 3: Custom components and CI/CD for TFX pipelines

Develop a CI/CD workflow with Cloud Build to build and deploy a TFX Pipeline.

  • Integrate Github trigger to trigger Cloud Build CI/CD workflow for a TFX pipeline.

Módulo 4: ML Metadata with TFX

Access and analyze pipeline artifacts in ML Metadata store.

Módulo 5: Continuous Training with multiple SDKs, KubeFlow & AI Platform Pipelines

Perform continuous training with Scikit-learn and AI Platform Pipelines

  • Perform continuous training with PyTorch and AI Platform Pipelines
  • Perform continuous training with XGBoost and AI Platform Pipelines
  • Perform continuous training with TensorFlow and AI Platform Pipelines

Módulo 6: Continuous Training with Cloud Composer

Perform continuous training with Cloud Composer

Módulo 7: ML Pipelines with MLflow

Manage Machine Learning lifecycle with MLflow

Módulo 8: Summary

Summarize the course

Documentación oficial para el curso ML Pipelines on Google Cloud.

  • Formador certificado por Google Cloud.
  • Más de 5 años de experiencia profesional.
  • Más de 4 años de experiencia docente.
  • Profesional activo en empresas del sector IT.

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Acerca del curso

El curso Advanced Machine Learning with TensorFlow on Google te brindará experiencia práctica en la optimización, implementación y escalado de una variedad de modelos de ML de producción. Aprenderás a crear sistemas de recomendación y modelos escalables, precisos y listos para producción para datos estructurados, datos de imágenes, series temporales y texto en lenguaje natural.

  • Ingenieros de datos y programadores interesados en aprender a poner en práctica el machine learning.
  • Cualquier persona interesada en aprender a construir y poner en funcionamiento modelos de TensorFlow.

  • Implementar los diversos tipos de sistemas de producción de ML: capacitación estática, dinámica y continua, inferencia estática y dinámica, y procesamiento por lotes y en línea.
  • Resolver un problema de ML mediante la creación de una canalización integral, desde la exploración de datos, el preprocesamiento, la ingeniería de características, la creación de modelos, el ajuste de hiperparámetros, la implementación y el servicio.
  • Desarrollar una variedad de modelos de clasificación de imágenes, desde modelos lineales simples hasta redes neuronales convolucionales (CNNs) de alto rendimiento con normalización por lotes, aumento y transferencia de aprendizaje.
  • Pronosticar valores de series temporales utilizando CNN, redes neuronales recurrentes (RNNs) y LSTMs.
  • Aplicar ML al texto en lenguaje natural utilizando CNNs, RNNs, LSTMs, incrustaciones de palabras reutilizables y modelos generativos de codificador y decodificador.
  • Implementar modelos de recomendación en TensorFlow, basados en contenido, colaborativos, híbridos y neuronales.

  • Tener conocimientos de machine learning y TensorFlow al nivel de especialización Machine Learning on Google Cloud.
  • Tener experiencia en codificación en Python.
  • Tener conocimiento de estadísticas básicas.
  • Tener conocimientos de SQL y computación en la nube.

Módulo 1: End-to-End Machine Learning with TensorFlow on GCP

Temas:

In the first course of this specialization, we recap what was covered in the Machine Learning on Google Cloud Specialization. One of the best ways to review something is to work with the concepts and technologies that you have learned. So, this course is run like a workshop where you will carry out end-toend machine learning with TensorFlow on Google Cloud Platform. Here you will learn how to explore large datasets for features, create training and evaluation datasets, build models with the Estimator API in TensorFlow, train at scale and deploy those models into production with Google Cloud Platform machine learning tools.

New learners with ML background can also follow this course to learn how to do ML on GCP to fast track to the more advanced topics coming soon under the advanced specialization.

Módulo 2: Production ML Systems

Temas:

We’ll cover how to implement the various flavors of production ML systems—static, dynamic, and continuous training; static and dynamic inference; and batch and online processing. We’ll delve into TensorFlow abstraction levels and the various options for doing distributed training and how to write distributed training models with custom estimators.

  • Compare static vs. dynamic training and inference
  • Manage model dependencies
  • Set up distributed training for fault tolerance, replication, and more
  • Export models for portability

Módulo 3: Image Classification Models

Temas:

We will take a look at different strategies for building an image classifier using convolutional neural networks. We’ll improve the model’s accuracy with augmentation, feature extraction, and fine-tuning hyperparameters while avoiding overfitting our data.

  • Classify images using deep learning
  • Implement convolutional neural networks
  • Improve the model by augmentation, batch normalization, etc.
  • Leverage transfer learning

Objetivos:

Gain an overview of how ML is applied to image classification, including the evolving methods and challenges

Módulo 4: Sequence Models

Temas:

  • Predict future values of a time-series
  • Classify free form text
  • Address time-series and text problems with recurrent neural networks
  • Choose between RNNs/LSTMs and simpler models
  • Train and reuse word embeddings in text problems

Objetivos:

This module is an introduction to sequence models and their applications, including an overview of sequence model architectures and how to handle inputs of variable length.

Módulo 5: Recommendation Models

Temas:

  • Devise a content-based recommendation engine
  • Implement a collaborative filtering recommendation engine
  • Build a hybrid recommendation engine with user and content embeddings

Objetivos:

Apply your knowledge of classification models and embeddings to build a ML pipeline that functions as a recommendation engine.

Documentación oficial para el curso Advanced Machine Learning with TensorFlow on Google.

  • Formador certificado por Google Cloud.
  • Más de 5 años de experiencia profesional.
  • Más de 4 años de experiencia docente.
  • Profesional activo en empresas del sector IT.

Solicita información


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Acerca del curso

En el curso Machine Learning on Google Cloud aprenderás a:

Este curso está dirigido, principalmente, a los siguientes participantes:

  • Aspirantes a analistas de datos de machine learning, científicos de datos e ingenieros de datos.
  • Personas que deseen aprender sobre machine learning mediante Vertex AI AutoML, BQML, Feature Store, Workbench, Dataflow, Vizier para el ajuste de hiperparámetros, y TensorFlow/Keras.

  • Crear, entrenar e implementar un modelo de machine learning sin escribir una sola línea de código usando Vertex AI AutoML.
  • Comprender cuándo usar AutoML y Big Query ML.
  • Crear conjuntos de datos gestionados por Vertex AI.
  • Agregar funciones a una Feature Store.
  • Describir Analytics Hub, Dataplex y Data Catalog.
  • Describir el ajuste de hiperparámetros con Vertex Vizier y cómo se puede utilizar para mejorar el rendimiento del modelo.
  • Crear un cuaderno administrado por el usuario de Vertex AI Workbench, un trabajo de capacitación personalizado y luego implementarlo usando un contenedor Docker.
  • Describir predicciones por lotes y en línea, y monitoreo de modelos.
  • Describir cómo mejorar la calidad de los datos.
  • Realizar análisis de datos exploratorios.
  • Construir y entrenar modelos de aprendizaje supervisado.
  • Optimizar y evaluar modelos utilizando funciones de pérdida y métricas de rendimiento.
  • Crear conjuntos de datos de prueba, evaluación y entrenamiento repetibles y escalables.
  • Implementar modelos ML usando TensorFlow/Keras.
  • Describir cómo representar y transformar características.
  • Comprender los beneficios de utilizar la ingeniería de funciones.
  • Explicar Vertex AI Pipelines.

  • Estar familiarizado con los conceptos básicos de machine learning.
  • Tener un dominio básico de un lenguaje de secuencias de comandos, preferiblemente Python.

Curso 1: How Google Does Machine Learning

What are best practices for implementing machine learning on Google Cloud? What is Vertex AI and how can you use the platform to quickly build, train, and deploy AutoML machine learning models without writing a single line of code? What is machine learning, and what kinds of problems can it solve?

Google thinks about machine learning slightly differently: it’s about providing a unified platform for managed datasets, a feature store, a way to build, train, and deploy machine learning models without writing a single line of code, providing the ability to label data, create Workbench notebooks using frameworks such as TensorFlow, SciKit Learn, Pytorch, R, and others. Our Vertex AI Platform also includes the ability to train custom models, build component pipelines, and perform both online and batch predictions. We also discuss the five phases of converting a candidate use case to be driven by machine learning, and consider why it is important to not skip the phases. We end with a recognition of the biases that machine learning can amplify and how to recognize them.

  • 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.

Curso 2: Launching into Machine Learning

The course begins with a discussion about data: how to improve data quality and perform exploratory data analysis. We describe Vertex AI AutoML and how to build, train, and deploy an ML model without writing a single line of code. You will understand the benefits of Big Query ML. We then discuss how to optimize a machine learning (ML) model and how generalization and sampling can help assess the quality of ML models for custom training.

  • 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.

Curso 3: TensorFlow on Google Cloud

The modules cover designing and building a TensorFlow input data pipeline, building ML models with TensorFlow and Keras, improving the accuracy of ML models, writing ML models for scaled use, and writing specialized ML models.

  • 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.

Curso 4: Feature Engineering

Want to know about Vertex AI Feature Store? Want to know how you can improve the accuracy of your ML models? What about how to find which data columns make the most useful features? Welcome to Feature Engineering, where we discuss good versus bad features and how you can preprocess and transform them for optimal use in your models. This course includes content and labs on feature engineering using BigQuery ML, Keras, and TensorFlow.

  • 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.

Curso 5: Machine Learning in the Enterprise

This course encompasses a real-world practical approach to the ML Workflow: a case study approach that presents an ML team faced with several ML business requirements and use cases. This team must understand the tools required for data management and governance and consider the best approach for data preprocessing: from providing an overview of Dataflow and Dataprep to using BigQuery for preprocessing tasks.

The team is presented with three options to build machine learning models for two specific use cases. This course explains why the team would use AutoML, BigQuery ML, or custom training to achieve their objectives.

A deeper dive into custom training is presented in this course. We describe custom training requirements from training code structure, storage, and loading large datasets to exporting a trained model.

You will build a custom training machine learning model, which allows you to build a container image with little knowledge of Docker.

The case study team examines hyperparameter tuning using Vertex Vizier and how it can be used to improve model performance. To understand more about model improvement, we dive into a bit of theory: we discuss regularization, dealing with sparsity, and many other essential concepts and principles. We end with an overview of prediction and model monitoring and how Vertex AI can be used to manage ML models.

  • 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.

Documentación oficial para el curso Machine Learning on Google Cloud.

  • Formador certificado por Google Cloud.
  • Más de 5 años de experiencia profesional.
  • Más de 4 años de experiencia docente.
  • Profesional activo en empresas del sector IT.

Solicita información


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Acerca del curso

El curso Google Cloud Big Data and Machine Learning Fundamentals presenta los productos y servicios de Big Data y Machine Leearning de Google Cloud que respaldan el ciclo de vida de datos a IA. Explora los procesos, los desafíos y los beneficios de crear una gran canalización de datos y modelos de machine learning con Vertex AI en Google Cloud.

  • Analistas de datos, científicos de datos y analistas de negocios que estén comenzando con Google Cloud.
  • Personas responsables de diseñar pipelines y arquitecturas para el procesamiento de datos, crear y mantener modelos estadísticos y de machine learning, consultar conjuntos de datos, visualizar resultados de consultas y crear informes.
  • Ejecutivos y tomadores de decisiones de TI que evalúen Google Cloud para que lo utilicen los científicos de datos.

  • Recognize the data-to-AI lifecycle on Google Cloud and the major products of big data and machine learning
  • Design streaming pipelines with Dataflow and Pub/Sub
  • Analyze big data at scale with BigQuery
  • Identify different options to build machine learning solutions on Google Cloud
  • Describe a machine learning workflow and the key steps with Vertex AI
  • Build a machine learning pipeline using AutoML

Tener conocimientos básicos de uno o más de los siguientes:

  • Lenguaje de consulta de base de datos como SQL.
  • Flujo de trabajo de ingeniería de datos desde extracción, transformación, carga hasta análisis, modelado e implementación.
  • Modelos de machine learning, como son modelos supervisados y no supervisados.

Módulo 0: Course Introduction

Temas:

This section welcomes learners to the Big Data and Machine Learning Fundamentals course and provides an overview of the course structure and goals.

Objetivos:

  • Recognize the data-to-AI lifecycle on Google Cloud
  • Identify the connection between data engineering and machine learning

Módulo 1: Big Data and Machine Learning on Google Cloud

Temas:

This section explores the key components of Google Cloud’s infrastructure. We introduce many of the big data and machine learning products and services tha support the data-to AI lifecycle on Google Cloud.

Objetivos:

  • Identify the different aspects of Google Cloud’s infrastructure.
  • Identify the big data and machine learning products on Google Cloud.

Módulo 2: Data Engineering for Streaming Data

Temas:

This section introduces Google Cloud’s solution to managing streaming data. It examines an end-to-end pipeline, including data ingestion with Pub/Sub, data processing with Dataflow, and data visualization with Looker and Data Studio.

Objetivos:

  • Describe an end-to-end streaming data workflow from ingestion to data visualization.
  • Identify modern data pipeline challenges and how to solve them at scale with Dataflow.
  • Build collaborative real-time dashboards with data visualization tools.

Módulo 3: Big Data with BigQuery

Temas:

This section introduces learners to BigQuery, Google’s fully managed, serverless data warehouse. It also explores BigQuery ML and the processes and key commands that are used to build custom machine learning models.

Objetivos:

  • Describe the essentials of BigQuery as a data warehouse.
  • Explain how BigQuery processes queries and stores data.
  • Define BigQuery ML project phases.
  • Build a custom machine learning model with BigQuery ML.

Módulo 4: Machine Learning Options on Google Cloud

Temas:

This section explores four different options to build machine learning models on Google Cloud. It also introduces Vertex AI, Google’s unified platform for building and managing the lifecycle of ML projects.

Objetivos:

  • Identify different options to build ML models on Google Cloud.
  • Define Vertex AI and its major features and benefits.
  • Describe AI solutions in both horizontal and vertical markets.

Módulo 5: The Machine Learning Workflow with Vertex AI

Temas:

This section focuses on the three key phases—data preparation, model training, and model preparation—of the machine learning workflow in Vertex AI. Learners can practice building a machine learning model with AutoML.

Objetivos:

  • Describe a ML workflow and the key steps.
  • Identify the tools and products to support each stage.
  • Build an end-to-end ML workflow using AutoML.

Módulo 6: Course Summary

Temas:

This section reviews the topics covered in the course and provides additional resources for further learning.

Objetivos:

Describe the data-to-AI lifecycle on Google Cloud and identify the major products of big data and machine learning.

Documentación oficial para el curso Google Cloud Big Data and Machine Learning Fundamentals.

  • Formador certificado por Google Cloud.
  • Más de 5 años de experiencia profesional.
  • Más de 4 años de experiencia docente.
  • Profesional activo en empresas del sector IT.

Solicita información


Descarga el programa del curso
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Hoja de Matriculación:
Descargar matrícula

Si no has encontrado lo que buscabas, prueba buscar tu curso o certificación aquí

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Descubre nuestras ofertas y promociones
Plan amigo, ven con un amigo y tienes descuento!
Plan Amigo
Desempleados. Si estás sin empleo tienes descuento!
Desempleado
Antiguos alumnos. Si has sido alumno de CAS Training tienes un descuento!
Antiguos Alumnos
Bonificación FUNDAE. Contamos con cursos boficados. Consúltanos!
Bonificación Fundae
Puedes pagar los cursos con Sodexo
Paga con Sodexo
Si tienes el Carné jóven de la comunidad de Madrid, tienes un descuento, consúltanos!
Carné Joven Comunidad de Madrid