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Google Cloud Fundamentals: Big Data & Machine Learning

This course introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. It explores the processes,
challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.

Objetivos

Neste curso, os participantes aprenderão as seguintes habilidades:

  • 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.

Público-Alvo

Esta aula destina-se ao seguinte público:

  • Data analysts, data scientists, and business analysts who are getting started with Google Cloud.
  • Individuals responsible for designing pipelines and architectures for data processing, creating and maintaining machine learning and statistical models, querying datasets, visualizing query results, and creating reports.
  • Executives and IT decision makers evaluating Google Cloud for use by data scientists.

Pré-requisitos

Para aproveitar ao máximo este curso, os participantes precisam atender aos seguintes critérios:

  • Database query language such as SQL.
  • Data engineering workflow from extract, transform, load, to analysis, modeling, and deployment.
  • Machine learning models such as supervised versus unsupervised models.

Duração

8 horas

Investimento

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Dependências de outros cursos e certificações com o curso de Google Cloud Fundamentals: Big Data & Machine Learning
Dependências de outros cursos e certificações com o curso de Google Cloud Fundamentals: Big Data & Machine Learning

Resumo do curso

O curso inclui apresentações, demonstrações e laboratórios práticos.
  • Recognize the data-to-AI lifecycle on Google Cloud.
  • Identify the connection between data engineering and machine learning.
  • Identify the different aspects of Google Cloud’s infrastructure.
  • Identify the big data and machine learning products on Google Cloud.
  • 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.
  • 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.
  • 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.
  • 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.
  • Describe the data-to-AI lifecycle on Google Cloud and identify the major products of big data and machine learning.