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Description
MLlib, the machine learning library of Apache Spark, is designed to be highly scalable and integrates effortlessly with Spark's various APIs, accommodating programming languages such as Java, Scala, Python, and R. It provides an extensive range of algorithms and utilities, which encompass classification, regression, clustering, collaborative filtering, and the capabilities to build machine learning pipelines. By harnessing Spark's iterative computation features, MLlib achieves performance improvements that can be as much as 100 times faster than conventional MapReduce methods. Furthermore, it is built to function in a variety of environments, whether on Hadoop, Apache Mesos, Kubernetes, standalone clusters, or within cloud infrastructures, while also being able to access multiple data sources, including HDFS, HBase, and local files. This versatility not only enhances its usability but also establishes MLlib as a powerful tool for executing scalable and efficient machine learning operations in the Apache Spark framework. The combination of speed, flexibility, and a rich set of features renders MLlib an essential resource for data scientists and engineers alike.
Description
Marathon serves as a robust container orchestration platform that integrates seamlessly with Mesosphere’s Datacenter Operating System (DC/OS) and Apache Mesos, ensuring high availability through its active/passive clustering and leader election mechanism, which guarantees continuous uptime. It supports multiple container runtimes, offering first-class integration for Mesos containers utilizing cgroups as well as Docker, making it adaptable to various development environments. Additionally, Marathon facilitates the deployment of stateful applications by allowing persistent storage volumes to be linked to your apps, which is particularly beneficial for running databases such as MySQL and Postgres with storage managed by Mesos. The platform boasts an intuitive and powerful user interface, along with a range of service discovery and load balancing options to suit diverse needs. Health checks are implemented to monitor application performance via HTTP or TCP checks, ensuring reliability. Users can also set up event subscriptions by providing an HTTP endpoint to receive notifications, which can aid in integrating with external load balancers. Lastly, metrics can be queried in JSON format at the /metrics endpoint, while also being capable of integration with popular systems like Graphite, StatsD, DataDog, or scraped using Prometheus, allowing for comprehensive monitoring and analysis of application performance. This combination of features positions Marathon as a versatile tool for managing containerized applications effectively.
API Access
Has API
API Access
Has API
Integrations
Apache Mesos
Amazon EC2
Apache Cassandra
Apache HBase
Apache Hive
Apache Spark
D2iQ
Datadog
Docker
Graphite Studio
Integrations
Apache Mesos
Amazon EC2
Apache Cassandra
Apache HBase
Apache Hive
Apache Spark
D2iQ
Datadog
Docker
Graphite Studio
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Apache Software Foundation
Founded
1995
Country
United States
Website
spark.apache.org/mllib/
Vendor Details
Company Name
D2iQ
Founded
2013
Country
United States
Website
mesosphere.github.io/marathon/
Product Features
Machine Learning
Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization