Google Cloud Big Data and Machine Learning Fundamentals

Length 1 day

Course overview

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Why study this course

This one-day 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.

Aligns to certification

Google Cloud Certified: Professional Data Engineer

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What you’ll learn

This course teaches participants the following skills:

Identify 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
Analyse 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

Google Cloud at Lumify Work

Lumify Work is Australia's only national Google Cloud Authorised Training Partner. Get the skills needed to build, test, and deploy applications on this highly scalable infrastructure. Engineered to handle the most data-intensive work you can throw at it, Lumify Work can support you through training wherever you are in your Cloud adoption journey.

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Who is the course for?

This course is intended for the following participants:

Data analysts, Data scientists, Business analysts 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, visualising query results and creating reports
Executives and IT decision makers evaluating Google Cloud for use by data scientists

Course subjects

Module 1: 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.

Recognise the data-to-AI lifecycle on Google Cloud
Identify the connection between data engineering and machine learning

Module 2: 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.

Identify how elements of the Google Cloud infrastructure have enabled big data and machine learning capabilities
Identify the big data and machine learning products on Google Cloud
Lab: Exploring a BigQuery Public Dataset

Module 3: 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 visualisation with Looker and Data Studio.

Describe an end-to-end streaming data workflow from ingestion to data visualisation.
Identify modern data pipeline challenges and how to solve them at scale
with Dataflow.
Build collaborative real-time dashboards with data visualisation tools
Lab: Creating a Streaming Data Pipeline for a Real-Time Dashboard with Dataflow

Module 4: Big Data with BigQuery
This section introduces learners to BigQuery, Google's fully managed, serverless data warehouse. It also explores BigQuery ML and the processes and key commands that are used to build custom machine learning models.

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.
Lab: Predicting Visitor Purchases Using BigQuery ML

Module 5: 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.

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.

Module 6: 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.

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

Module 7: Course Summary
This section reviews the topics covered in the course and provides additional resources for further learning.

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

Prerequisites

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

Terms & Conditions

The supply of this course by Lumify Work is governed by the booking terms and conditions. Please read the terms and conditions carefully before enrolling in this course, as enrolment in the course is conditional on acceptance of these terms and conditions.