In this one-day instructor-led course, participants will learn about Big Data and Machine Learning features of the Google Cloud Platform. Through a combination of presentations, demos and hands-on labs, attendees will have an overview of Google Cloud Platform and a detailed view of data processing and machine learning capabilities. This course shows the ease, flexibility and power of Big Data solutions on Google Cloud Platform.
In this course, participants will learn the following skills:
- Identify the purpose and value of the main Big Data and machine learning products on Google Cloud Platform.
- Use Cloud SQL and Cloud Dataproc to migrate current MySQL and Hadoop / Pig / Spark / Hive workloads to the Google Cloud Platform.
- Use BigQuery and Cloud Datalab to perform interactive data analysis.
- Train and use a neural network with TensorFlow.
- Employ ML APIs.
- Choose between different data processing products on Google Cloud Platform.
This class is aimed at the following audience:
- Data analysts, data scientists, business analysts who are starting to use Google Cloud Platform
- Individuals responsible for creating pipelines and architectures for data processing, creating and maintaining machine learning and statistical models, querying data sets, viewing query results and creating reports
- IT decision makers and executives who evaluate Google Cloud Platform for use by data scientists
To fully benefit from this course, participants must comply with the following criteria:
- Basic proficiency with common query language, such as SQL;
- Experience with data modeling, extraction, transformation and loading activities;
- Application development with common programming language, such as Python;
- Familiarity with machine learning and / or statistics.
The course includes presentations, demonstrations and hands-on labs.
- Google Platform Fundamentals Overview.
- Google Cloud Platform Big Data Products.
- CPUs on demand (Compute Engine).
- A global filesystem (Cloud Storage).
- Lab: Set up a Ingest-Transform-Publish data processing pipeline.
- 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.
- Fast random access.
- Lab: Build machine learning dataset.
- Machine Learning with TensorFlow.
- Lab: Carry out ML with TensorFlow
- Pre-built models for common needs.
- Lab: Employ ML APIs
- Message-oriented architectures with Pub/Sub.
- Creating pipelines with Dataflow.
- Reference architecture for real-time and batch data processing.
- Why GCP?
- Where to go from here
- Additional Resources