---
type: concept
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    - concepts-data-science-and-machine-learning-on-databricks-databricks-on-aws.md
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  aliases:
    - ml-vs-deep-learning-vs-genai-distinctions
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  citations:
    - file: concepts-data-science-and-machine-learning-on-databricks-databricks-on-aws.md
title: ML vs Deep Learning vs GenAI Distinctions
description: The overlapping boundaries between classical machine learning, deep learning, and generative AI, with platform features like Model Serving, ai_query, AI Runtime, MLflow, and AI Search supporting all three paradigms.
tags:
  - machine-learning
  - deep-learning
  - generative-ai
timestamp: "2026-06-18T11:06:35.373Z"
---

# ML vs Deep Learning vs GenAI Distinctions

**Machine learning (ML)** is a broad field of techniques that enable systems to extract insight and build predictive models from data. Classic ML includes methods like classification, regression, anomaly detection, forecasting, and recommendation. Both **deep learning** (DL) and **generative AI** (GenAI) are technically subsets of ML — deep learning uses multi-layer neural networks, while GenAI focuses on models that generate new content.^[concepts-data-science-and-machine-learning-on-databricks-databricks-on-aws.md]

## Relationship Between the Fields

The boundaries between machine learning, deep learning, and generative AI can be fuzzy in practice. Many modern tools and platforms support all three paradigms without requiring users to choose a single category.^[concepts-data-science-and-machine-learning-on-databricks-databricks-on-aws.md]

A useful way to think about the relationship:

- **ML** is the broadest category, encompassing any system that learns from data. This includes everything from linear regression to random forests to large language models.
- **Deep learning** (DL) is a subfield of ML that uses multi-layer neural networks. It excels at handling unstructured data (images, text, audio) and has enabled many recent advances in AI.
- **Generative AI** (GenAI) builds on both ML and deep learning, but is distinguished by its ability to generate new content — text, images, code, or other outputs — that resembles training data. Not all deep learning is generative (e.g., classifiers are not), and not all generative AI requires deep learning (though most modern systems do).

See also [machine learning lifecycle](/concepts/cicd-for-machine-learning.md) for how these techniques fit into end-to-end workflows.^[concepts-data-science-and-machine-learning-on-databricks-databricks-on-aws.md]

## Platform Features That Support All Three

The following Databricks platform features support all three paradigms — classic ML, deep learning, and GenAI — without requiring separate tooling:^[concepts-data-science-and-machine-learning-on-databricks-databricks-on-aws.md]

- [Model Serving](/concepts/model-serving.md) — supports classic ML, deep learning, and custom GenAI models for both real-time and batch inference.
- ai_query — supports SQL queries and batch inference workloads for all three paradigms, including calling custom models.
- [AI Runtime](/concepts/ai-runtime.md) and GPU-enabled [Databricks Runtime for Machine Learning](/concepts/databricks-runtime-for-machine-learning.md) — support training and fine-tuning across all three paradigms.
- [MLflow Experiment Tracking](/concepts/mlflow-experiment-tracking.md) — tracks runs and experiments for all three paradigms.
- Databricks AI Search — serves unstructured data for all three use cases.

## Key Distinctions at a Glance

| Dimension | Classic ML | Deep Learning | Generative AI |
|---|---|---|---|
| **Core technique** | Statistical models, decision trees, ensembles | Multi-layer neural networks | Models that can create new content |
| **Data needs** | Often works with structured data | Excels with unstructured data (text, images, audio) | Large unstructured datasets for training |
| **Common outputs** | Predictions, classifications, scores | Predictions, embeddings, classifications | Generated text, images, code, audio |
| **Example algorithms** | Regression, random forest, SVM | Convolutional networks, transformers | GPT, DALL·E, Stable Diffusion |

These categories are not mutually exclusive. A generative AI model like GPT is simultaneously a deep learning model and a machine learning model. The distinctions matter most for understanding capability requirements (e.g., GPU compute for deep learning) and governance needs (e.g., content safety for GenAI).^[concepts-data-science-and-machine-learning-on-databricks-databricks-on-aws.md]

## Related Concepts

- Machine learning lifecycle — The end-to-end process from data to production
- Data science and ML on Databricks — Overview of Databricks ML capabilities
- GenAI on Databricks — Specific guidance for generative AI workflows
- [ML Platform](/concepts/ml-platform.md) — Infrastructure supporting all three paradigms
- Deep learning — The neural network subfield of ML
- [Generative AI](/concepts/mlflow-tracing-for-generative-ai.md) — The content-generation branch of AI

## 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)
