Google Cloud Fundamentals: Big Data and Machine Learning (GCF-BDM)
Classroom: 550€ +Iva
REF: GCF-BDM Catálogo: Google Cloud Área: Google Cloud Fundamentals
Duração:
1 dia
Próxima Data:
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Descrição
This one-day instructor-led course introduces participants to the big data capabilities of Google Cloud Platform. Through a combination of presentations, demos, and hands-on labs, participants get an overview of the Google Cloud platform and a detailed view of the data processing and machine learning capabilities. This course showcases the ease, flexibility, and power of big data solutions on Google Cloud Platform.
Curso disponível em Live Training
Destinatários
- Data analysts, Data scientists, Business analysts getting started with Google Cloud Platform.
- 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 Platform for use by data scientists.
Área: Google Cloud
Certificação Associada: This course is part of the following Certifications: Google Cloud Certified Professional Machine Learning Engineer (PMLE), Google Cloud Certified Professional Data Engineer (PDE)
*Curso disponível em Live Training
Programa:
Module 1: Introducing Google Cloud Platform
- Google Platform Fundamentals Overview.
- Google Cloud Platform Big Data Products.
Module 2: Compute and Storage Fundamentals
- CPUs on demand (Compute Engine).
- A global filesystem (Cloud Storage).
- Cloud Shell.
- Lab: Set up an Ingest-Transform-Publish data processing pipeline.
Module 3: Data Analytics on the Cloud
- Stepping-stones to the cloud.
- Cloud SQL: your SQL database on the cloud.
- Lab: Importing data into CloudSQL and running queries.
- Spark on Dataproc.
- Lab: Machine Learning Recommendations with Spark on Dataproc.
Module 4: Scaling Data Analysis
- Fast random access.
- Datalab.
- BigQuery.
- Lab: Build machine learning dataset.
Module 5: Machine Learning
- Machine Learning with TensorFlow.
- Lab: Carry out ML with TensorFlow
- Pre-built models for common needs.
- Lab: Employ ML APIs.
Module 6: Data Processing Architectures
- Message-oriented architectures with Pub/Sub.
- Creating pipelines with Dataflow.
- Reference architecture for real-time and batch data processing.
Module 7: Summary
- Why GCP?
- Where to go from here
- Additional Resources
Pré-requisitos:
- Basic proficiency with common query language such as SQL.
- Experience with data modeling, extract, transform, load activities.
- Developing applications using a common programming language such Python.
- Familiarity with machine learning and/or statistics.
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