Google Cloud Big Data & Machine Learning Fundamentals (GCPBDML)

2023/1/13 (FRI)

09:30 – 17:30

(Total 8 training hours)

2/F, The Whitney, 183 Wai Yip Street, Kwun Tong (Online class available)

Cantonese, Supplemented with English terminology

HK$4,500

Course Information

Course Name: Google Cloud Big Data & Machine Learning Fundamentals

Trainer: Zorro Cheng

Certificate: A minimum of 70% attendance rate is required for awarding of a completion certificate

Remark: Please Bring your own laptop (BYOD) to classes.

What Will You Achieve

  • 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

Who Is This Course For?

  • Planned to deploy applications and create application environments on Google Cloud
  • Developers, systems operations professionals, and solution architects getting started with Google Cloud
  • Executives and business decision makers evaluating the potential of Google Cloud to address their business needs

Requirement

Basic understanding of one or more of the following:

  • 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

Course Outline

  • 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.
  • Lab: Vertex AI: Predicting Loan Risk with AutoML
  • Quiz

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

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