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.