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Pick the path that matches your transition goal: production AI systems, MLOps, GenAI, or Databricks.

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Specialized tracks designed for different AI career goals

AI Engineering Career Transition

Role-ready systems, portfolio, and interview positioning.

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MLOps

Pipelines, deployment, observability, and production workflows.

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GenAI/LLM Systems

RAG, evaluation, orchestration, and practical guardrails.

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Databricks/Data Engineering

Distributed data, ML lifecycle, and lakehouse workflows.

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Featured Learning Path

Azure Databricks ML & Data Engineering

A comprehensive, hands-on curriculum covering distributed data systems, ML lifecycle management, and production-grade data & ML engineering on Azure Databricks.

120-150 hours

40+ projects

Certificate

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Learning Phases

40+

Hands-on Projects

150h

Total Duration

100%

Practical Focus

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machine-learning

Databricks for Beginners

This hands-on course is designed to take you from beginner to advanced practitioner in Databricks, the unified data and AI platform. Through...

₹24,999

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