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.
challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.
Objetivos
En este curso, los participantes aprenderán las siguientes 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
Esta clase está dirigida a la siguiente audiencia:
- 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.
Prerrequisitos
Para aprovechar al máximo este curso, los participantes deben cumplir con los siguientes criterios:
- 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.
Duración
8 horas
Inversión
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Resumen del curso
El curso incluye presentaciones, demostraciones y laboratorios prácticos.
- 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.