> ## Documentation Index
> Fetch the complete documentation index at: https://docs.kinetica.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Machine Learning Concepts

<a id="ml-concepts" />

*Kinetica* provides a machine learning (ML) capability for simplifying and
accelerating data science in a scalable fashion. With ML, users can ingest data,
train models, make inferences (answers/output from models), and even audit
models.  ML leverages *Kubernetes* to deploy, train, and test models.

<Info>
  To work with models in SQL, see [Machine Learning (ML)](/content/sql/ml#sql-ml).  For
  statistical analysis functions that don't require a model, see
  [ML Functions](/content/sql/query#sql-ml-functions).
</Info>

## Concepts

The ML workflow is defined by five key concepts:

* [Registries](#registries)
* [Data](#data)
* [Models](#models)
* [Deployments](#deployments)
* [Audits](#audits)

<a id="ml-registry" />

### Registries

*Docker container registries* house the ML models themselves and abstract their
implementation details.  Any model used by *Kinetica* must exist in a
*Docker container registry*.

<a id="ml-data" />

### Data

*Data* comprises *ingests*, *datasets*, and *feature sets*.

* *Ingests* represent an ingest tool (e.g., *Kafka*) that pulls
  data from a given source (*Kinetica*, *PostgreSQL*, *S3*, etc.) and puts it
  in a new table inside *Kinetica*. Data can be pulled in batches
  or via continuous streaming.
* *Datasets* represent a set (or sub-set) of column data from a source table
  in *Kinetica*. One or more columns can be filtered to create a *dataset*.
* *Feature sets* represent a group of features, which are *datasets*
  transformed inline with functions or relationally using
  [Materialized Views](/content/concepts/materialized_views)

<a id="ml-model" />

### Models

A *model* can be a function, statistical model, regression, data model, and more
that is deployed to enable inferencing capabilities. *Kinetica* can deploy any
number of replicas of the model, allowing for scalability and better resource
management. Currently, only *Blackbox models* are supported, which are models
where implementation details are abstracted and housed in *Docker* containers.
Input and output are the only available interface; they also don't require a
training *dataset*.

<a id="ml-deployment" />

### Deployments

A *deployment* represents a *model* that has been deployed. A *deployed model*
can have inference tests run manually, automatically, or in batches, depending
on the type of *deployment*. Currently there are three types of *deployments*:

* *On-Demand* -- inference tests are run as necessary using user-provided input
  with results being returned based on the given input
* *Continuous* -- inference tests are run automatically against records being
  streamed into an input table; inference results are inserted into an output
  table
* *Batch* -- inference tests are run against a batch of data in an existing
  table all at once

<a id="ml-audit" />

### Audits

An *audit* represents the ability to *audit* a *model* deployment to ensure its
training, testing, and inferencing are untampered. An *audit* enables drilling
into specific inferences from a deployment and filtering the inference by input
parameter, process status, and more.

<a id="kml-install" />

## Installation

*Kinetica* recommends installing *KML*
[using KAgent](/content/install/kagent_install), as *KAgent* can install
everything *KML* requires and can preconfigure everything for the best
out-of-the-box experience. Consult [Cluster](/content/install/kagent_install#kagent-ui-cluster) for *KML* package
install instructions using *KAgent*. Since *KML* relies on *Kubernetes*, an external
*Kubernetes* cluster needs to be provided; *KAgent* will configure *KML* to
communicate with the external *Kubernetes* cluster .

<a id="kml-logging" />

## Logging

All the main logs for the *KML* service are located in
<Badge color="gray">/opt/gpudb/kml/logs</Badge>.