Curso Google Cloud Big Data and Machine Learning Fundamentals

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.

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.

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

Módulo 0: Course Introduction
This section welcomes learners to the Big Data and Machine Learning Fundamentals course and provides an overview of the course structure and goals.

Módulo 1: Big Data and Machine Learning on Google Cloud
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.

Módulo 2: Data Engineering for Streaming Data
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.

Módulo 3: Big Data with BigQuery
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.

Módulo 4: Machine Learning Options on Google Cloud
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.

Módulo 5: The Machine Learning Workflow with Vertex AI
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.

Módulo 6: Course Summary
This section reviews the topics covered in the course and provides additional resources for further learning.