Google Cloud Big Data and Machine Learning Fundamentals

The Google Cloud Big Data and Machine Learning Fundamentals course introduces students to the powerful big data and machine learning capabilities of Google Cloud. During the course, students will have the opportunity to familiarize themselves with Google Cloud, with a focus on its data processing capabilities. They will be introduced to key technologies such as Cloud SQL and Dataproc, tools that allow them to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud. Additionally, the course covers the use of BigQuery and Cloud SQL for interactive data analysis and building ML models with BigQuery ML, ML APIs, and AutoML. This course helps prepare students for the Google Cloud Certified Professional Data Engineer and Google Cloud Certified Professional Machine Learning Engineer certification exams .

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Course Objectives

Below is a summary of the main objectives of the Google Cloud Big Data and Machine Learning Fundamentals Course :

Get familiar with Google Cloud’s big data and machine learning capabilities.
Learn how to use Cloud SQL and Dataproc to migrate workloads.
Analyze interactive data with BigQuery.
Build machine learning models with BigQuery ML, ML APIs, and AutoML.
Gain practical skills in processing and analyzing large volumes of data.
Explore how to use Dataflow for stream and batch processing.
Understand how to implement data pipelines with Cloud Data Fusion.
Learn to visualize data insights with Looker and Data Studio.

Course Certification

This course helps you prepare to take the:
Google Cloud Certified Professional Data Engineer Exam;

Course Outline

Module 1: Introducing Google Cloud Platform

Google Platform Fundamentals Overview.
Google Cloud Platform Big Data Products.
Lab: Sign up for Google Cloud Platform.

Module 2: Compute and Storage Fundamentals

CPUs on demand (Compute Engine).
A global file system (Cloud Storage).
Cloud Shell.
Lab: Set up an Ingest-Transform-Publish data processing pipeline.

Module 3: Data Analytics on the Cloud

Stepping stones to the cloud.
Cloud SQL: your SQL database on the cloud.
Lab: Importing data into CloudSQL and running queries.
Spark on Dataproc.
Lab: Machine Learning Recommendations with Spark on Dataproc.

Module 4: Scaling Data Analysis

Fast random access.
Computer Lab.
BigQuery.
Lab: Build a Machine Learning Dataset.

Module 5: Machine Learning

Machine Learning with TensorFlow.
Lab: Carry out ML with TensorFlow.
Pre-built models for common needs.
Lab: Employ ML APIs.

Module 6: Data Processing Architectures

Message-oriented architectures with Pub/Sub.
Creating pipelines with Dataflow.
Reference architecture for real-time and batch data processing.

Module 7: Summary

Why GCP?.
Where to go from here.
Additional Resources

Course Mode

Instructor-Led Remote Live Classroom Training;

Trainers

Trainers are GCP Official Instructors and certified in other IT technologies, with years of hands-on experience in the industry and in Training.

Lab Topology

For all types of delivery, the Trainee can access real Cisco equipment and systems in our laboratories or directly at the Cisco data centers remotely 24 hours a day. Each participant has access to implement the various configurations thus having a practical and immediate feedback of the theoretical concepts.
Here are some Labs topologies available:

Course Details

Course Prerequisites

Basic understanding of cloud computing concepts, familiarity with IT infrastructure, and some experience with Google Cloud Platform or another cloud provider..

Course Duration

Intensive duration 1 days

Course Frequency

Course Duration: 1 days (9.00 to 17.00) - Ask for other types of attendance.

Course Date

Google Cloud Big Data and Machine Learning Fundamentals Course (Intensive Formula) – On request – 09:00 – 17:00

Steps to Enroll

Registration takes place by asking to be contacted from the following link, or by contacting the office at the international number +355 45 301 313 or by sending a request to the email info@hadartraining.com