Google Cloud Big Data and Machine Learning Fundamentals Training in Russia

To fully benefit from this course, learners should have:

• A basic understanding of database query languages such as SQL.
• Familiarity with data engineering workflows, including extract, transform, load (ETL) processes.
• A conceptual grasp of machine learning models, particularly supervised and unsupervised learning.

Target Audience

This course is designed for:

• Data analysts, data scientists, and business analysts beginning their journey with Google Cloud.
• Individuals responsible for data processing pipeline design, machine learning model development, and data visualization.
• Executives and IT decision-makers evaluating Google Cloud's big data and AI capabilities.

By the end of this course, participants will be able to:

• Identify the data-to-AI lifecycle and major big data and machine learning products in Google Cloud.
• Design streaming data pipelines using Dataflow and Pub/Sub.
• Analyse large datasets at scale with BigQuery and create machine learning models using BigQuery ML.
• Explore various machine learning solutions on Google Cloud and implement workflows using Vertex AI and AutoML.

Module One: Introduction to big data and machine learning on Google Cloud

• Overview of Google Cloud's infrastructure and data-to-AI lifecycle.
• Key products supporting big data and AI.
• Activities:
  • Lab: Exploring a BigQuery Public Dataset.
  • Quiz.

Module Two: Data engineering for streaming data

• Managing streaming data with Pub/Sub and Dataflow.
• Creating data visualizations with Looker and Data Studio.
• Activities:
  • Lab: Building a streaming data pipeline for real-time dashboard creation.
  • Quiz.

Module Three: Big data analysis with BigQuery

• BigQuery essentials for large-scale data storage and processing.
• Introduction to BigQuery ML for custom machine learning models.
• Activities:
  • Lab: Predicting visitor purchases using BigQuery ML.
  • Quiz.

Module Four: Machine learning options on Google Cloud

• Overview of machine learning solutions available on Google Cloud.
• Introduction to Vertex AI for unified ML project management.
• Activities:
  • Quiz.

Module Five: Machine learning workflow with Vertex AI

• Understanding the stages of a machine learning workflow: data preparation, model training, and deployment.
• Practical application using Vertex AI and AutoML.
• Activities:
  • Lab: Predicting loan risk using AutoML with Vertex AI.
  • Quiz.

Module Six: Course summary and additional resources

• Review of key concepts and tools.
• Guidance for continued learning in Google Cloud's big data and machine learning offerings.