Overview

This learning path is designed for professionals who need a practical understanding of cloud environments used in analytics, data engineering, and machine learning delivery. The emphasis is not on certification cram, but on how cloud services fit into real data workflows.

What the path covers

  • Core cloud concepts for analytics and ML teams
  • Storage, compute, orchestration, and managed services
  • Data pipelines and scripting patterns across cloud platforms
  • Environment setup for experimentation, training, and deployment
  • Comparative use cases for AWS, GCP, Azure, and Databricks

Typical modules

  • Cloud fundamentals and architecture patterns
  • AWS services for data processing and ML workloads
  • GCP services for data platforms and applied AI pipelines
  • Azure services for BI, enterprise integration, and ML operations
  • Databricks for collaborative analytics and scalable notebooks

Good fit for

  • Data teams moving from local workflows to cloud execution
  • Companies building internal data or ML platforms
  • Professionals who need a structured entry point into multi-cloud data work

Delivery options

This topic can be delivered as an internal workshop, modular academy, architecture-oriented training series, or blended learning path combined with labs and implementation support.