100 Days of Machine Learning on Databricks Day 8: How Databricks Simplifies the Machine Learning Workflow

Machine Learning in the enterprise is hard.

You deal with:

• Messy and distributed data sources like SAP and Customer 360
• Multiple teams and tools across the lifecycle
• The challenge of scaling from notebooks to production pipelines

That’s where Databricks becomes a game-changer.

It provides a unified platform that connects data, analytics, ML, and GenAI — turning silos into a seamless Machine Learning Factory.

The Traditional ML Stack is Fragmented

Before Databricks, ML workflows typically required:

• A data warehouse for storage
• ETL tools for prep
• Notebooks for experimentation
• Separate infrastructure for training
• Yet another stack for model serving
• And manual governance on top

This fragmentation led to delays, duplication, and poor collaboration.

Databricks Unifies the Stack