---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: 77b8ee74bc0d17adc1a4df28e585b49a4fb8ffcbc20454dbc3d4a234d0edcf4f
  pageDirectory: concepts
  sources:
    - concepts-data-science-and-machine-learning-on-databricks-databricks-on-aws.md
  confidence: 0.85
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - difference-between-ml-deep-learning-and-genai
    - GenAI and Difference Between ML, Deep Learning,
    - DBMDLAG
  citations:
    - file: concepts-data-science-and-machine-learning-on-databricks-databricks-on-aws.md
title: Difference Between ML, Deep Learning, and GenAI
description: Conceptual boundaries between classic machine learning, deep learning, and generative AI, noting that deep learning and GenAI are technically types of ML with fuzzy boundaries.
tags:
  - machine-learning
  - deep-learning
  - genai
timestamp: "2026-06-19T17:49:59.262Z"
---

# Difference Between ML, Deep Learning, and GenAI

**Machine learning (ML)** is the broad field of building predictive models from data using techniques such as classification, regression, anomaly detection, forecasting, and recommendation. It covers the entire lifecycle from scoping use cases to monitoring production models. ^[concepts-data-science-and-machine-learning-on-databricks-databricks-on-aws.md]

**Deep learning** is a subset of ML that uses multi‑layer neural networks to learn from data. Modern deep learning and generative AI methods are technically types of ML. ^[concepts-data-science-and-machine-learning-on-databricks-databricks-on-aws.md]

**Generative AI (GenAI)** is a further subset of deep learning focused on models that generate new content — text, images, code, or other data — often based on large language models (LLMs) or other generative architectures. The boundaries between ML, deep learning, and GenAI can be fuzzy, but they share the same foundational platform capabilities. ^[concepts-data-science-and-machine-learning-on-databricks-databricks-on-aws.md]

## Relationship Overview

| Layer | Description |
|-------|-------------|
| **Machine Learning** | Umbrella term for all data‑driven predictive and analytical techniques. |
| **Deep Learning** | A subset of ML that uses neural networks with multiple layers. |
| **Generative AI** | A subset of deep learning that generates novel outputs (e.g., text, images). |

Modern GenAI methods are technically a type of ML, but the distinction is often drawn in practice because of the scale, architecture, and use cases involved. ^[concepts-data-science-and-machine-learning-on-databricks-databricks-on-aws.md]

## Platform Support for All Three Paradigms

Databricks provides a unified platform that supports all three paradigms with the same core components: ^[concepts-data-science-and-machine-learning-on-databricks-databricks-on-aws.md]

- **[Model Serving](/concepts/model-serving.md)** – Deploy and serve classic ML, deep learning, and custom GenAI models for real‑time or batch inference.
- **`ai_query`** – Run SQL queries and batch inference workloads against any model type.
- **[AI Runtime](/concepts/ai-runtime.md) and GPU‑enabled [Databricks Runtime for Machine Learning](/concepts/databricks-runtime-for-machine-learning.md)** – Provide training and fine‑tuning environments for all three paradigms.
- **[MLflow Experiment Tracking](/concepts/mlflow-experiment-tracking.md)** – Track runs and experiments for all three paradigms.
- **Databricks AI Search** – Index and serve unstructured data, supporting all three paradigms.

The platform documentation focuses on ML and deep learning; for dedicated GenAI guidance, see the [Concepts: Generative AI on Databricks](https://docs.databricks.com/aws/en/agents/concepts/) page. ^[concepts-data-science-and-machine-learning-on-databricks-databricks-on-aws.md]

## Related Concepts

- Machine Learning Lifecycle
- [Deep learning best practices on Databricks](/concepts/deep-learning-best-practices-on-databricks.md)
- [Generative AI on Databricks](/concepts/ai-runtime-ai-v5-on-databricks.md)
- [Feature Store](/concepts/feature-store.md)
- [Model Serving](/concepts/model-serving.md)

## Sources

- concepts-data-science-and-machine-learning-on-databricks-databricks-on-aws.md

# Citations

1. [concepts-data-science-and-machine-learning-on-databricks-databricks-on-aws.md](/references/concepts-data-science-and-machine-learning-on-databricks-databricks-on-aws-6175a64a.md)
