Google Cloud Big Data and Machine Learning Fundamentals
- Created By raju2006
- Last Updated December 1st, 2023
- Overview
- Prerequisites
- Audience
- Audience
- Curriculum
Description:
Explore Google Cloud's big data capabilities in a single-day, instructor-led course. Discover the world of big data through engaging presentations, live demonstrations, and hands-on labs. Gain a comprehensive understanding of Google Cloud's data processing and machine learning capabilities. Experience the remarkable ease, flexibility, and robustness of big data solutions on Google Cloud in this immersive session.
Course Code/Duration:
BDT101 / 1 Day
Learning Objectives:
This course teaches participants the following skills:
- Identify the purpose and value of the key Big Data and Machine Learning products on Google Cloud.
- Use Cloud SQL and Cloud Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud.
- 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 Google Cloud.
To get the most of out of this course, participants should have:
- Basic proficiency with common query language such as SQL.
- Experience with data modelling, extract, transform, load activities.
- Developing applications using a common programming language such as Python.
- Familiarity with machine learning and/or statistics.
This class is intended for the following:
- Data analysts, Data scientists, Business analysts getting started with Google Cloud.
- Individuals are 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 for use by data scientists.
This class is intended for the following:
- Data analysts, Data scientists, Business analysts getting started with Google Cloud.
- Individuals are 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 for use by data scientists.
Course Outline:
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).
- 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.
- 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.
- Datalab.
- 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
Training material provided:
Yes (Digital format)
The curriculum is empty
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