Course Description

Course Overview

The Google Cloud Big Data and Machine Learning Fundamentals (GCF-BDM) course is designed to provide individuals with a comprehensive introduction to big data and machine learning concepts and how they can be leveraged on the Google Cloud Platform (GCP). This course covers the foundational principles of big data processing, analytics, and machine learning using GCP’s tools and services.

Prerequisites

To enroll in the GCF-BDM course, participants should have a basic understanding of data processing and analysis concepts. Familiarity with programming languages and SQL is beneficial but not mandatory. Prior knowledge of cloud computing and GCP fundamentals, as covered in the Google Cloud Fundamentals course, is recommended.

Methodology

The GCF-BDM course employs a combination of theoretical lectures, demonstrations, and hands-on labs to facilitate effective learning. Participants will engage in instructor-led sessions where big data and machine learning concepts are explained in detail. They will also have access to GCP resources and tools to gain practical experience in working with big data and machine learning technologies. The course encourages active participation, discussions, and collaborative problem-solving to reinforce learning.

Course Outline

Introduction to Big Data and Machine Learning on GCP

Overview of big data and machine learning concepts

Introduction to GCP’s big data and machine learning services

GCP Data Storage and Processing

Cloud Storage for data storage and retrieval

BigQuery for analytics and querying large datasets

Cloud Dataproc for big data processing

GCP Machine Learning Services

Introduction to machine learning and its applications

Cloud ML Engine for training and deploying machine learning models

AutoML for automated machine learning

GCP Dataflow and Data Transformation

Dataflow for real-time and batch data processing

Dataflow programming model and pipelines

Dataflow templates and transformations

GCP Big Data Analytics

BigQuery ML for machine learning within BigQuery

BigQuery Data Transfer Service for data ingestion

Data Studio for data visualization and reporting

GCP AI Platform

AI Platform Notebooks for data exploration and development

AI Platform Training for distributed training of machine learning models

AI Platform Prediction for serving predictions at scale

Outcome

By the end of the GCF-BDM course, participants will have:

• Developed a comprehensive understanding of big data and machine learning concepts
• Gained familiarity with GCP’s big data and machine learning services and tools
• Acquired knowledge of data storage, processing, and analysis using GCP services
• Learned best practices for machine learning model training and deployment
• Gained hands-on experience through practical labs and exercises
• Prepared for further specialization in big data or machine learning on GCP or relevant certification exams

Labs

The GCF-BDM course includes hands-on labs that provide participants with practical experience in working with big data and machine learning on GCP. Some examples of lab exercises include:

• Storing and retrieving data from Cloud Storage
• Performing data analytics and querying using BigQuery
• Setting up and running data processing jobs with Cloud Dataproc
• Training and deploying a machine learning model with Cloud ML Engine
• Implementing automated machine learning using AutoML
• Building and executing data pipelines with Dataflow
• Using BigQuery ML to create machine learning models within BigQuery
• Exploring and analyzing data with AI Platform Notebooks

These labs enable participants to apply the concepts learned in the course and gain hands-on experience with big data and machine learning technologies on GCP, allowing them to develop practical skills in working with large datasets and applying machine learning techniques.