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.
- 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.
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.
The course includes presentations, demonstrations, and hands-on labs.
- Module 1: Introducing Google Cloud Platform
- Explain the advantages of Google Cloud Platform
- Define the components of Google’s network infrastructure, including Points of presence data centers, regions, and zones
- Understand the difference between Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS)
- Module 2: Getting Started with Google Cloud Platform
- Identify the purpose of projects on Googlue Cloud Platform
- Understand the purpose of and use cases for Identity and Access Management
- List the methods of interacting with Google Cloud Platform
- Lab: Getting Started with Google Cloud Platform
- Module 3: Virtual Machines and Networks in the Cloud
- Identify the purpose of and use cases for Google Compute Engines
- Understand the various Google Cloud Platform networking and operational
- tools and services.
- Lab: Compute Engine
- Module 4: Storage in the Cloud
- Understand the purpose of and use cases for Google Cloud Storage, Google
- Cloud SQL, Google Cloud Bigtable, and Google Cloud Datastore.
- Learn how to choose between the various storage options on Google Cloud
- Lab: Cloud Storage and Cloud SQL
- Module 5: Containers in the Cloud
- Define the concept of a container and identify uses for containers.
- Identify the purpose of and use cases for Google Kubernetes Engine and
- Lab: Kubernetes Engine
- Module 6: Applications in the Cloud
- Understand the purpose of and use cases for Google App Engine.
- Contrast the App Engine Standard environment with the App Engine Flexible
- Understand the purpose of and use cases for Google Cloud Endpoints.
- Lab: App Engine
- Module 7: Developing, Deploying, and Monitoring in the Cloud
- Understand options for software developers to host their source code.
- Understand the purpose of template-based creation and management of resources.
- Understand the purpose of integrated monitoring, alerting, and debugging.
- Lab: Deployment Manager and Stackdriver
- Module 8: Big Data and Machine Learning in the Cloud
- Understand the purpose of and use cases for the products and services in the Google Cloud big data and machine learning platforms.
- Lab: BigQuery
For more information about this course, feel free to drop us your questions by filling in the form on the right side.