Google Cloud Fundamentals: Big Data and Machine Learning Training

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 Python.
• Familiarity with machine learning and/or statistics.

• 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.

• 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.

Module 1: Introducing Google Cloud Platform

• Google Platform Fundamentals Overview.
• Google Cloud Platform Data Products and Technology.
• Usage scenarios.
• Lab: Sign up for Google Cloud Platform.

Module 2: Compute and Storage Fundamentals

• CPUs on demand (Compute Engine).
• A global filesystem (Cloud Storage).
• CloudShell.
• Lab: Set up a 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 SparkML.

Module 4: Scaling Data Analysis

• Fast random access.
• Datalab.
• BigQuery.
• Lab: Build machine learning dataset.
• Machine Learning with TensorFlow.
• Lab: Train and use neural network.
• Fully built models for common needs.
• Lab: Employ ML APIs

Module 5: Data Processing Architectures

• Message-oriented architectures with Pub/Sub.
• Creating pipelines with Dataflow.
• Reference architecture for real-time and batch data processing.

Module 6: Summary

• Why GCP?
• Where to go from here
• Additional Resources