Google Cloud: Big Data And Machine Learning Fundamentals
- Course Code GO8325
- Duration 1 day
Course Delivery
Additional Payment Options
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GTC 10 inc. VAT
GTC, Global Knowledge Training Credit, please contact Global Knowledge for more details
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Course Delivery
This course is available in the following formats:
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Company Event
Event at company
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Public Classroom
Traditional Classroom Learning
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Virtual Learning
Learning that is virtual
Request this course in a different delivery format.
Course Overview
TopVirtual Learning
This interactive training can be taken from any location, your office or home and is delivered by a trainer. This training does not have any delegates in the class with the instructor, since all delegates are virtually connected. Virtual delegates do not travel to this course, Global Knowledge will send you all the information needed before the start of the course and you can test the logins.
Course Schedule
TopTarget Audience
Top- Data analysts getting started with Google Cloud Platform
- Data scientists getting started with Google Cloud Platform
- 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
Course Objectives
TopIn this course you will learn:
- Purpose and value of the key Big Data and Machine Learning products in the GoogleCloud Platform
- Use Cloud SQL and Cloud Dataproc to migrate existing MySQL andHadoop/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
Course Content
Top1. Introducing Google Cloud Platform
- Google Platform Fundamentals Overview
- Google Cloud Platform Data Products and Technology
- Usage scenarios
2. Compute and Storage Fundamentals
- CPUs on demand (Compute Engine)
- A global filesystem (Cloud Storage)
- CloudShell
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
4. Scaling Data Analysis
- Fast random access
- Datalab
- BigQuery
- Machine Learning with TensorFlow
- Fully built models for common needs
5. Data Processing Architectures
- Message-oriented architectures with Pub/Sub
- Creating pipelines with Dataflow
- Reference architecture for real-time and batch data processing
6. Summary
- Why GCP?
- Where to go from here
- Additional Resources
Classroom Live Labs
Lab 1: Sign up for Google Cloud Platform
Lab 2: Set up a Ingest-Transform-Publish data processing pipeline
Lab 3: Machine Learning Recommendations with SparkML
Lab 4: Build machine learning dataset
Lab 5: Train and use neural network
Lab 6: Employ ML APIs
Course Prerequisites
Top- 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 Python
- Familiarity with Machine Learning and/or statistics
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