Overview
This course introduces participants to the big data capabilities of Google Cloud. Through a combination of presentations, demos, and hands-on labs, participants get an overview of Google Cloud 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.
Skills Covered
- Identify the purpose and value of Google Cloud products and services.
- Interact with Google Cloud services.
- Describe ways in which customers have used Google Cloud.
- Use Big Data and ML products on Google Cloud: App Engine, Google Kubernetes Engine, and Compute Engine.
- Make use of BigQuery, Google’s managed data warehouse for analytics.
Who Should Attend
- 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.
Course Curriculum
Course Modules
Exam & Certification
The Google Cloud Big Data and Machine Learning Fundamentals training is associated with 2 Google Cloud certifications and the optional exam add-on is valid for either both tracks:
Google Cloud Professional Machine Learning Engineer.
A Professional Machine Learning Engineer designs, builds, and productionizes ML models to solve business challenges using Google Cloud technologies and knowledge of proven ML models and techniques. ML Engineers consider responsible AI throughout the ML development process, and collaborates closely with other job roles to ensure long-term success of models.
Google Cloud Professional Data Engineer
A Professional Data Engineer enables data-driven decision making by collecting, transforming, and publishing data. A data engineer should be able to design, build, operationalize, secure, and monitor data processing systems with a particular emphasis on security and compliance; scalability and efficiency; reliability and fidelity; and flexibility and portability. A data engineer should also be able to leverage, deploy, and continuously train pre-existing machine learning models.
