Virtual Classroom
GCP Fundamentals: Big Data & Machine Learning
4th November, 2024 • 9.00 am-5.00pm CST
Join Jellyfish & Google for a free-of-charge, one-day, virtual, instructor-led training session, to help you start your learning and certification journey on Google Cloud.
Jellyfish has recently collected awards for both the Regional ATP of the Year for North America and Regional ATP of the Year for EMEA as selected by Google Cloud.
This course will help you understand the big data capabilities of the Google Cloud. It introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle, and explores the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.
Through expert guidance and practical labs, you’ll gain an overview of Google Cloud and a detailed view of its data processing and machine learning capabilities. This course showcases the ease, flexibility, and power of big data solutions on Google Cloud.
This Google Cloud Fundamentals: Big Data & Machine Learning course is part of the Professional Data Engineer track.
Due to the nature and content of this course, all delegates are requested to stay engaged and participate fully in the virtual classroom. This course contains exercises to complete within Qwiklabs, and all delegates will be required to complete these exercises as part of the course.
Register for virtual classroom
GCP Fundamentals: Big Data & Machine Learning – 4th November, 2024
Who should attend:
This course is intended for the following participants:
- 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
Walk away with the ability to:
- Recognize the data-to-AI lifecycle on Google Cloud and the major products of big data and machine learning
- Design streaming pipelines with Dataflow and Pub / Sub
- Analyze big data at scale with BigQuery
- Identify different options to build machine learning solutions on Google Cloud.
- Describe a machine learning workflow and the key steps with Vertex AI.
- Build a machine learning pipeline using AutoML
Prerequisites:
To get the most out of this course, you should have:
- Basic proficiency with common query language such as SQL
- Data engineering workflow from extract, transform, load, to analysis, modeling, and deployment
- Machine learning models such as supervised versus unsupervised models