Google Cloud Big Data & Machine Learning Fundamentals (GC-GCBDMLF)
This course will introduce you to Google Cloud's big data and machine learning functions. You'll begin with a quick overview of Google Cloud and then dive deeper into its data processing capabilities.
Roughly one year of experience with one or more of the following:
- A common query language such as SQL.
- Extract, transform, and load activities.
- Data modeling.
- Machine learning and/or statistics.
- Programming in Python.
- Identify the purpose and value of the key Big Data and Machine Learning products in Google Cloud.
- Use Cloud SQL and Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud.
- Employ BigQuery and Cloud SQL to carry out interactive data analysis.
- Choose between different data processing products in Google Cloud.
- Create ML models with BigQuery ML, ML APIs, and AutoML.
- Data analysts, data scientists, and business analysts who are getting started with Google Cloud.
- 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 for use by data scientists.
The course includes presentations, demonstrations, and hands-on labs.
Module 1: Introducing Google Cloud Platform
- Google Platform Fundamentals Overview.
- Google Cloud Platform Big Data Products.
- Lab: Sign up for Google Cloud Platform.
Module 2: Compute and Storage Fundamentals
- CPUs on demand (Compute Engine).
- A global file system (Cloud Storage).
- Cloud Shell.
- 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.
- 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.