只提供英語內容
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.
Course Objectives
- 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.
Course Outline
Module 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.
Module 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 that support the data-to AI lifecycle on Google Cloud.
Module 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.
Module 3: Big Data with BigQuery
This section introduces learners to BigQuery, Google’s fully managed, serverlessdata warehouse. It also explores BigQuery ML and the processes and key commands that are used to build custom machine learning models
Module 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.
Module 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.
Module 6 : Course Summary
This section reviews the topics covered in the course and provides additional resources for further learning.
Date & Time
Date: 10 Aug 2024 (Sat)
Time: 09:30 – 17:00 (Total 6 training hours)
Medium
Cantonese, supplemented with English terminology
Course Fee
FREE
Course Fee
Online Broadcast
Prerequisite
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.
Bring Your Own Device (BYOD): Windows 7/10 / Mac OS 10.x or above with minimum 2 GB RAM and 20 GB hard disk
Who Should Attend?
- 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