MLlib

MLlib

Apache Software Foundation
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About

MLflow is an open source platform to manage the ML lifecycle, including experimentation, reproducibility, deployment, and a central model registry. MLflow currently offers four components. Record and query experiments: code, data, config, and results. Package data science code in a format to reproduce runs on any platform. Deploy machine learning models in diverse serving environments. Store, annotate, discover, and manage models in a central repository. The MLflow Tracking component is an API and UI for logging parameters, code versions, metrics, and output files when running your machine learning code and for later visualizing the results. MLflow Tracking lets you log and query experiments using Python, REST, R API, and Java API APIs. An MLflow Project is a format for packaging data science code in a reusable and reproducible way, based primarily on conventions. In addition, the Projects component includes an API and command-line tools for running projects.

About

​Apache Spark's MLlib is a scalable machine learning library that integrates seamlessly with Spark's APIs, supporting Java, Scala, Python, and R. It offers a comprehensive suite of algorithms and utilities, including classification, regression, clustering, collaborative filtering, and tools for constructing machine learning pipelines. MLlib's high-quality algorithms leverage Spark's iterative computation capabilities, delivering performance up to 100 times faster than traditional MapReduce implementations. It is designed to operate across diverse environments, running on Hadoop, Apache Mesos, Kubernetes, standalone clusters, or in the cloud, and accessing various data sources such as HDFS, HBase, and local files. This flexibility makes MLlib a robust solution for scalable and efficient machine learning tasks within the Apache Spark ecosystem. ​

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

Companies looking for an open source platform solution for the machine learning lifecycle

Audience

Data scientists and engineers wanting a machine learning solution for efficient data processing and analysis within the Apache Spark framework

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Pricing

No information available.
Free Version
Free Trial

Pricing

No information available.
Free Version
Free Trial

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

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Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

MLflow
Founded: 2018
United States
mlflow.org

Company Information

Apache Software Foundation
Founded: 1995
United States
spark.apache.org/mllib/

Alternatives

Union Cloud

Union Cloud

Union.ai

Alternatives

Apache Spark

Apache Spark

Apache Software Foundation
Apache Mahout

Apache Mahout

Apache Software Foundation
DVC

DVC

iterative.ai
Amazon EMR

Amazon EMR

Amazon

Categories

Categories

Integrations

Apache Spark
Kubernetes
Amazon EC2
Apache HBase
Apache Hive
Apache Mesos
Apolo
Axolotl
Cranium
CrateDB
Databricks
Google Cloud Platform
IBM watsonx.data integration
Kedro
Keras
OpenMetadata
Scala
TrueFoundry
Vectice
conDati

Integrations

Apache Spark
Kubernetes
Amazon EC2
Apache HBase
Apache Hive
Apache Mesos
Apolo
Axolotl
Cranium
CrateDB
Databricks
Google Cloud Platform
IBM watsonx.data integration
Kedro
Keras
OpenMetadata
Scala
TrueFoundry
Vectice
conDati
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Claim MLflow and update features and information
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