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Google Cloud Fundamentals: Big Data and Machine Learning
GOOGLE PROFESSIONAL PROFESSIONAL MACHINE LEARNING CLOUD, IOT, machinelearning, bigdata, virtualmachines, deeplearning, bigquery, virtualcontainers, app, iaas, paas, saas, ml apis, cloudshell, cloudsql, dataengineer, monitoring, computeengine
€2000.00 (Pre-Order)
DURATION
1 day
DESCRIPTION
This one-day instructor-led course introduces participants to the big data capabilities of Google Cloud Platform. Through a combination of presentations, demos, and hands-on labs, participants get an overview of the Google Cloud platform and a detailed view of the data processing and machine learning capabilities. This course showcases the ease, flexibility, and power of big data solutions on Google Cloud Platform.
OBJECTIVES
This course teaches participants the following skills:
Identify the purpose and value of the key Big Data and Machine Learning products in the Google Cloud Platform
Use Cloud SQL and Cloud Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud Platform
Employ BigQuery and Cloud Datalab to carry out interactive data analysis
Train and use a neural network using TensorFlow
Employ ML APIs
Choose between different data processing products on the Google Cloud Platform
AUDIENCE
This class is intended for the following participants:
Data analysts, Data scientists, Business analysts getting started with Google Cloud Platform
Individuals responsible for designing pipelines and architectures for data processing, creating and maintaining machine learning and statistical models, querying datasets, visualizing query results and creating reports
Executives and IT decision makers evaluating Google Cloud Platform for use by data scientists
PREREQUISITES
To get the most of out of this course, participants should have:
Basic proficiency with common query language such as SQL
Experience with data modeling, extract, transform, load activities
Developing applications using a common programming language such as Python
Familiarity with Machine Learning and/or statistics
TOPICS
Module 1: Introducing Google Cloud Platform
Google Platform Fundamentals Overview
Google Cloud Platform Big Data Products
Module 2: Compute and Storage Fundamentals
CPUs on demand (Compute Engine)
A global filesystem (Cloud Storage)
CloudShell
Lab: Set up an Ingest-Transform-Publish data processing pipeline
Module 3: Data Analytics on the Cloud
Stepping-stones to the cloud
CloudSQL: 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
Datalab
BigQuery
Lab: Build 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 
1 day
DESCRIPTION
This one-day instructor-led course introduces participants to the big data capabilities of Google Cloud Platform. Through a combination of presentations, demos, and hands-on labs, participants get an overview of the Google Cloud platform and a detailed view of the data processing and machine learning capabilities. This course showcases the ease, flexibility, and power of big data solutions on Google Cloud Platform.
OBJECTIVES
This course teaches participants the following skills:
Identify the purpose and value of the key Big Data and Machine Learning products in the Google Cloud Platform
Use Cloud SQL and Cloud Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud Platform
Employ BigQuery and Cloud Datalab to carry out interactive data analysis
Train and use a neural network using TensorFlow
Employ ML APIs
Choose between different data processing products on the Google Cloud Platform
AUDIENCE
This class is intended for the following participants:
Data analysts, Data scientists, Business analysts getting started with Google Cloud Platform
Individuals responsible for designing pipelines and architectures for data processing, creating and maintaining machine learning and statistical models, querying datasets, visualizing query results and creating reports
Executives and IT decision makers evaluating Google Cloud Platform for use by data scientists
PREREQUISITES
To get the most of out of this course, participants should have:
Basic proficiency with common query language such as SQL
Experience with data modeling, extract, transform, load activities
Developing applications using a common programming language such as Python
Familiarity with Machine Learning and/or statistics
TOPICS
Module 1: Introducing Google Cloud Platform
Google Platform Fundamentals Overview
Google Cloud Platform Big Data Products
Module 2: Compute and Storage Fundamentals
CPUs on demand (Compute Engine)
A global filesystem (Cloud Storage)
CloudShell
Lab: Set up an Ingest-Transform-Publish data processing pipeline
Module 3: Data Analytics on the Cloud
Stepping-stones to the cloud
CloudSQL: 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
Datalab
BigQuery
Lab: Build 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
