GB
/
GBP
/
EN_GB

Shaping the future of IT skills

Maximising IT performance through learning

Google Cloud Fundamentals: Big Data and Machine Learning

WGAC-GGL-GCF-BDM

Google
Open

Google Cloud Fundamentals: Big Data and Machine Learning

11 Jul 2022 - 1 day

German

CET UTC+01:00

£562

Open

Google Cloud Fundamentals: Big Data and Machine Learning

25 Jul 2022 - 1 day

Italian

CET UTC+01:00

£562

Open

Google Cloud Fundamentals: Big Data and Machine Learning

08 Aug 2022 - 1 day

German

CET UTC+01:00

£562

Open

Google Cloud Fundamentals: Big Data and Machine Learning

12 Sep 2022 - 1 day

German

CET UTC+01:00

£562

Open

Google Cloud Fundamentals: Big Data and Machine Learning

12 Sep 2022 - 1 day

English

GMT UTC+00:00

£625

Open

Google Cloud Fundamentals: Big Data and Machine Learning

03 Oct 2022 - 1 day

Italian

CET UTC+01:00

£562

Description

Show Tabs
Introduction
Module 1: Big Data and Machine Learning on Google Cloud
  • Identify the different aspects of Google Cloud’s infrastructure.
  • Identify the big data and machine learning products on Google Cloud.
  • Lab: Exploring a BigQuery Public Dataset
Module 2: Data Engineering for Streaming Data
  • Describe an end-to-end streaming data workflow from ingestion to data visualization.
  • Identify modern data pipeline challenges and how to solve them at scale with Dataflow.
  • Build collaborative real-time dashboards with data visualization tools.
  • Lab: Creating a Streaming Data Pipeline for a Real-Time Dashboard with Dataflow
Module 3: Big Data with BigQuery
  • Describe the essentials of BigQuery as a data warehouse.
  • Explain how BigQuery processes queries and stores data.
  • Define BigQuery ML project phases.
  • Build a custom machine learning model with BigQuery ML.
  • Lab: Predicting Visitor Purchases Using BigQuery ML
Module 4: Machine Learning Options on Google Cloud
  • Identify different options to build ML models on Google Cloud.
  • Define Vertex AI and its major features and benefits.
  • Describe AI solutions in both horizontal and vertical markets.
Module 5: The Machine Learning Workflow with Vertex AI
  • Describe a ML workflow and the key steps.
  • Identify the tools and products to support each stage.
  • Build an end-to-end ML workflow using AutoML.
  • Lab: Vertex AI: Predicting Loan Risk with AutoML
Module 5: Course Summary

This section reviews the topics covered in the course and provides additional resources for further learning.

Describe the data-to-AI lifecycle on Google Cloud and identify the major products of big data and machine learning.

Prerequisites & Audience

Basic understanding of one or more of the following:

  • Database query language such as SQL
  • Data engineering workflow from extract, transform, load, to analysis, modeling, and deployment
  • Machine learning models such as supervised versus unsupervised models
Course Benefits

This course teaches participants the following skills:

  • Recognize the data-to-AI lifecycle on Google Cloud and the major products of big data and machine learning.
  • Design streaming pipelines with Dataflow and Pub/Sub.
  • Analyze big data at scale with BigQuery.
  • Identify different options to build machine learning solutions on Google Cloud.
  • Describe a machine learning workflow and the key steps with Vertex AI.
  • Build a machine learning pipeline using AutoML.
Course Topics
  • Module 1: Big Data and Machine Learning on Google Cloud
  • Module 2: Data Engineering for Streaming Data
  • Module 3: Big Data with BigQuery
  • Module 4: Machine Learning Options on Google Cloud
  • Module 5: The Machine Learning Workflow with Vertex AI
  • Module 6: Course Summary

Google courses


Cloud Digital Leader
CODE: WGAC-GGL-CDL
Data Integration with Cloud Data Fusion
CODE: WGAC-GGL-DICDF
Preparing for Your Professional Cloud Network Engineer Journey
CODE: WGAC-GGL-PYPCNEJ
Deploying and Managing Windows Workloads on Google Cloud
CODE: WGAC-GGL-DMWWGC
Installing and Managing Google Cloud's Apigee API Platform for Private Cloud
CODE: WGAC-GGL-IMAPIPC
Customer Experiences with Contact Center AI - Dialogflow CX
CODE: WGAC-GGL-CCAIDCX
Customer Experiences with Contact Center AI - Dialogflow ES
CODE: WGAC-GGL-CCAIDES
Application Development with Cloud Run
CODE: WGAC-GGL-ADCR
Serverless Data Processing with Dataflow
CODE: WGAC-GGL-SDPF
Developing Data Models with LookML
CODE: WGAC-GGL-DDMLML
Analyzing and Visualizing Data with Looker
CODE: WGAC-GGL-AVDL
Machine Learning on Google Cloud
CODE: WGAC-GGL-MLGC
Developing APIs with Google Cloud's Apigee API platform
CODE: WGAC-GGL-T-APIENG-B
Managing Google Cloud's Apigee API Platform for Hybrid Cloud
CODE: WGAC-GGL-T-APIHYB-B
Logging, Monitoring, and Observability in Google Cloud
CODE: WGAC-GGL-LMOGC
Security in Google Cloud Platform
CODE: WGAC-GGL-SGCP-3D
Google Cloud Fundamentals for Azure Professionals
CODE: WGAC-GGL-GCPAZURE
Preparing for the Associate Cloud Engineer Examination
CODE: WGAC-GGL-PPACE
Architecting Hybrid Cloud Infrastructure with Anthos
CODE: WGAC-GGL-T-AHYBRID-I
Architecting with Google Kubernetes Engine
CODE: WGAC-GGL-AGKE
Architecting with Google Compute Engine
CODE: WGAC-GGL-AGCE
Preparing for the Professional Data Engineer Examination
CODE: WGAC-GGL-PPDEE
Networking in Google Cloud Platform
CODE: WGAC-GGL-NGCP
Preparing for the Professional Cloud Architect Examination
CODE: WGAC-GGL-PPCAE
Getting Started with Google Kubernetes Engine
CODE: WGAC-GGL-GCP-GSGKE
Google Cloud Platform Fundamentals for AWS Professionals
CODE: WGAC-GGL-GCP-FAP
Developing Applications with Google Cloud Platform
CODE: WGAC-GGL-DAGCP
From Data to Insights with Google Cloud Platform
CODE: WGAC-GGL-DIGCP
Data Engineering on Google Cloud Platform
CODE: WGAC-GGL-DEGCP
Google Cloud Fundamentals: Big Data and Machine Learning
CODE: WGAC-GGL-GCF-BDM
Architecting with Google Cloud Platform: Design and Process
CODE: WGAC-GGL-AGCP-DP
Google Cloud Fundamentals: Core Infrastructure
CODE: WGAC-GGL-GCF-CI
We use cookies to understand how you use our site and to improve your experience. To learn more, click here. Read our revised Privacy Policy and Terms and Conditions.