Google Cloud Platform Big Data and Machine Learning Fundamentals

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This course introduces participants to the Big Data and Machine Learning capabilities of Google Cloud. Event is only available in Europe .

Through expert guidance and practical labs, you’ll gain 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.This Google Cloud Platform Fundamentals: Big Data &amp; Machine Learning course is part of the Professional Data Engineer track.The training will be given in 2 half days.<h3>Course overview</h3>Target audience:<ul><li>This course is intended for the following participants:</li><li>Data analysts, Data scientists, Business analysts getting started with Google Cloud Platform</li><li>Individuals responsible for designing pipelines and architectures for data processing, creating and maintaining machine learning and statistical models, querying datasets, visualising query results and creating reports</li><li>Executives and IT decision makers evaluating Google Cloud Platform for use by data scientists</li></ul>Objectives:<ul><li>Identify the purpose and value of the key Big Data and Machine Learning products in the Google Cloud Platform</li><li>Use Cloud SQL and Cloud Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud Platform</li><li>Employ BigQuery and Cloud Datalab to carry out interactive data analysis</li><li>Train and use a neural network using TensorFlow</li><li>Employ ML APIs</li><li>Choose between different data processing products on the Google Cloud Platform</li></ul>Prerequisites:<ul><li>To get the most of out of this course, you should have:</li><li>Basic proficiency with common query language such as SQL</li><li>Experience with data modeling, extract, transform, load activities</li><li>Developing applications using a common programming language such Python</li><li>Familiarity with machine learning and/or statistics</li></ul><h3>Content</h3>Module 1: Introducing Google Cloud Platform<ul><li>Google Platform Fundamentals Overview</li><li>Google Cloud Platform Big Data Products</li></ul>Module 2: Compute and Storage Fundamentals<ul><li>CPUs on demand (Compute Engine)</li><li>A global filesystem (Cloud Storage)</li><li>CloudShell</li><li>Lab: Set up a Ingest-Transform-Publish data processing pipeline</li></ul>Module 3: Data Analytics on the Cloud<ul><li>Stepping-stones to the cloud</li><li>Cloud SQL: your SQL database on the cloud</li><li>Lab: Importing data into CloudSQL and running queries</li><li>Spark on Dataproc</li><li>Lab: Machine Learning Recommendations with Spark on Dataproc</li></ul>Module 4: Scaling Data Analysis<ul><li>Fast random access</li><li>Datalab</li><li>BigQuery</li><li>Lab: Build machine learning dataset</li></ul>Module 5: Machine Learning<ul><li>Machine Learning with TensorFlow</li><li>Lab: Carry out ML with TensorFlow</li><li>Pre-built models for common needs</li><li>Lab: Employ ML APIs</li></ul>Module 6: Data Processing Architectures<ul><li>Message-oriented architectures with Pub/Sub</li><li>Creating pipelines with Dataflow</li><li>Reference architecture for real-time and batch data processing</li></ul>Module 7: Summary<ul><li>Why GCP?</li><li>Where to go from here</li><li>Additional Resources</li></ul>

Google Cloud Platform Big Data and Machine Learning Fundamentals

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Google Cloud Platform Big Data and Machine Learning Fundamentals

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