Big Data and Machine Learning with Google Cloud Platform

Big Data and Machine Learning with Google Cloud Platform Outline

Module 1: Introduction to Google Cloud Platform 

• Google Platform Fundamentals Overview 

• Google Cloud Platform Big Data Products 
   

Module 2: Compute and Storage Fundamentals 

• CPUs on Demand (Compute Engine) 

• Global Filesystem (Cloud Storage) 

• CloudShell 

• Lab: Set Up an Ingest Transform Publish Data Processing Pipeline 
   

Module 3: Data Governance and Security 

• Importance of Data Governance 

• Data Security Best Practices 

• Compliance and Regulatory Considerations 

• Data Encryption and Access Controls 

• Monitoring and Auditing Data Access 

• Lab: Implementing Data Governance and Security 
   

Module 4: Data Analytics on the Cloud 

• Stepping-Stones to the Cloud 

• Cloud SQL: Your SQL Database on the Cloud 

• Spark on Dataproc 
   

Module 5: Scaling Data Analysis 

• Fast Random Access 

• Datalab 

• Lab: Build a Machine Learning Dataset 
   

Module 6: Machine Learning 

• Introduction to TensorFlow Datasets 

• Pre-built Models for Common Needs 
   

Module 7: Data Processing Architectures 

• Message-Oriented Architectures with Pub/Sub 

• Reference Architecture for Real-Time and Batch Data Processing

What You’ll Learn in this Course

This course takes you from understanding cloud-based data analytics to designing and deploying machine learning solutions. Each stage builds technical proficiency in Google Cloud’s tools and services. 
 

• Learn the fundamentals of Google Cloud data and ML services 

• Learn how to process and analyse datasets using BigQuery and Dataflow 

• Learn to build and train machine learning models using TensorFlow on GCP 

• Learn how to implement scalable pipelines for data processing and analytics 

• Learn to automate model deployment and monitoring in cloud environments 

• Learn how to apply AI and ML insights for business decision-making