Databricks & GenAI
Databricks combines data engineering and ML workflows, often used to prepare and process data feeding model training or RAG ingestion.
Overview
Databricks combines a Spark-based processing engine with notebooks, ML tooling, and a data lakehouse, which makes it a common single platform for the data-preparation, fine-tuning, and evaluation work that sits upstream of an AI application.
Where It Fits
Lakehouse (Raw Data)
Data Processing
Fine-Tuning / Evaluation
Model or Embeddings
Key Points
- Lakehouse
- Databricks combines data-warehouse-style structure with data-lake-style flexibility for large, mixed datasets.
- One platform, multiple stages
- Data cleaning, embedding generation, fine-tuning, and evaluation can all run on the same underlying compute.
- MLflow integration
- Databricks’ MLflow tooling is commonly used to track fine-tuning runs and evaluation results.
Interview Question
What role does a platform like Databricks play in an AI system, compared to the model provider itself?
The model provider serves inference; Databricks typically handles everything upstream of that — cleaning and joining raw data, generating embeddings in bulk, running fine-tuning jobs, and tracking evaluation results — on one platform rather than several disconnected tools.
Explain It in 30 Seconds
Databricks combines Spark-based data processing with ML tooling on a lakehouse, making it a common single platform for the data preparation, fine-tuning, and evaluation work that happens before a model is ever called in production.
Real-World Stack
Technologies commonly used to implement this in production.