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Jan 1

Google Cloud Big Data and Machine Learning Fundamentals

January 1 @ 8:00 AM - 5:00 PM AEST

Google Cloud logo

GOOGLE CLOUD big data and machine learning fundamentals

This instructor led live training will introduce you to Google Cloud’s big data and machine learning functions. You’ll begin with a quick overview of Google Cloud and then dive deeper into its data processing capabilities.

What you Will learn

What's Included?

Instructor Live Training

An instructor will answer your questions

OFFICIAL GOOGLE CLOUD CONTENT

Course content reflects the latest google cloud class

hands on labs

Real world hands on labs provided by Qwiklabs and supported by instructor

CertIficate of completion

Receive official certificate on completion of 80% of labs

Who's this course for?

LeveL

Language

duration

Prerequisites

Products

Course Content

Topics

  • Google Cloud Infrastructure
  • Compute Power
  • Storage
  • Networking
  • Security
  • Big Data and Machine Learning Products

Objectives

  • Identify the different aspects of Google Cloud’s infrastructure
  • Identify the big data and ML products that form Google Cloud

Activities

  • Explore a customer use case
  • Lab 1: Exploring a Public Dataset with BigQuery

Topics

  • Recommendation Systems
  • Choosing the Right Solution Approach
  • On-premise to Google Cloud
  • Off-cluster Storage with Google Cloud Storage
  • Storing Recommendations

Objectives

  • Review how businesses use recommendation models
  • Evaluate how and where you will compute and store your housing rental model results
  • Analyze how running Hadoop in the cloud with Dataproc can enable scale
  • Evaluate different approaches for storing recommendation data off-cluster

Activities

  • Lab 2: Recommending Products using Cloud SQL and Spark

Topics

  • Introduction to BigQuery
  • Insights from Geographic Data
  • Machine Learning on Structured Data
  • Creating ML Models with SQL
  • Key Features Walkthrough

Objectives

  • Analyze big data at scale with BigQuery
  • Learn how BigQuery processes queries and stores data at scale
  • Walkthrough key ML terms: features, labels, training data
  • Evaluate the different types of models for structured datasets
  • Create custom ML models with BigQuery ML

Activities

  • Lab 3: Predicting Visitor Purchases w/BigQuery ML

Topics

  • Modern Data Pipeline Challenges
  • Message-oriented Architectures
  • Serverless Data Pipelines
  • Designing Streaming Pipelines With Apache Beam
  • Implementing Streaming Pipelines on Dataflow
  • Data Visualization With Data Studio
  • Tips and Tricks to Create Charts With the Data Studio UI

Objectives

  • Identify modern data pipeline challenges and how to solve them at scale with Dataflow
  • Design streaming pipelines with Apache Beam
  • Build collaborative real-time dashboards with Data Studio

Activities

  • Lab 4: Creating a Streaming Data Pipeline for a Real-Time Dashboard with Dataflow

Topics

  • ML Drives Business Value
  • How Does ML on Unstructured Data Work?
  • Choosing the Right ML Approach
  • Pre-built AI Building Blocks
  • Customizing Pre-built Models with AutoML
  • Building a Custom Model

Objectives

  • Evaluate how businesses use unstructured ML models and how the models work
  • Choose the right approach for machine learning models between pre-built and custom
  • Create a high-performing custom image classification model with no code using AutoML

Activities

  • Lab 5: Classifying Images of Clouds in the Cloud with AutoML Vision

Topics

  • Recap of key learning points
  • Resources

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Have Questions?

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Ref: T-GCBDML-B-02

Location

Online

Instructor

Axalon Academy
Email:
training@axalon.io
View Instructor Website

Other

Competencies
Beginner
Learning Path
Data Analyst, Data Engineer, Data Scientist