chronon

# Chronon: A Data Platform for AI/ML

Chronon is a platform that abstracts away the complexity of data computation and serving for AI/ML applications. Users define features as transformation of raw data, then Chronon can perform batch and streaming computation, scalable backfills, low-latency serving, guaranteed correctness and consistency, as well as a host of observability and monitoring tools.

It allows you to utilize all of the data within your organization, from batch tables, event streams or services to power your AI/ML projects, without needing to worry about all the complex orchestration that this would usually entail.

More information about Chronon can be found at [chronon.ai](https://chronon.ai/).

![High Level](https://chronon.ai/_images/intro.png)


## Platform Features

### Online Serving

Chronon offers an API for realtime fetching which returns up-to-date values for your features. It supports:

- Managed pipelines for batch and realtime feature computation and updates to the serving backend
- Low latency serving of computed features 
- Scalable for high fanout feature sets

### Backfills 

ML practitioners often need historical views of feature values for model training and evaluation. Chronon's backfills are:

- Scalable for large time windows
- Resilient to highly skewed data
- Point-in-time accurate such that consistency with online serving is guaranteed

### Observability, monitoring and data quality

Chronon offers visibility into:

- Data freshness - ensure that online values are being updated in realtime
- Online/Offline consistency - ensure that backfill data for model training and evaluation is consistent with what is being observed in online serving 

### Complex transformations and windowed aggregations

Chronon supports a range of aggregation types. For a full list see the documentation [here](https://chronon.ai/Aggregations.html).

These aggregations can all be configured to be computed over arbitrary window sizes.

# Quickstart

This section walks you through the steps to create a training dataset with Chronon, using a fabricated underlying raw dataset.

Includes:
- Example implementation of the main API components for defining features - `GroupBy` and `Join`.
- The workflow for authoring these entities.
- The workflow for backfilling training data.
- The workflows for uploading and serving this data.
- The workflow for measuring consistency between backfilled training data and online inference data.

Does not include:
- A deep dive on the various concepts and terminologies in Chronon. For that, please see the [Introductory](https://chronon.ai/authoring_features/GroupBy.html) documentation.
- Running streaming jobs.

## Requirements

- Docker

## Setup

To get started with the Chronon, all you need to do is download the [docker-compose.yml](https://github.com/airbnb/chronon/blob/main/docker-compose.yml) file and run it locally:

```bash
curl -o docker-compose.yml https://chronon.ai/docker-compose.yml
docker-compose