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Description
IBM Analytics for Apache Spark offers a versatile and cohesive Spark service that enables data scientists to tackle ambitious and complex inquiries while accelerating the achievement of business outcomes. This user-friendly, continually available managed service comes without long-term commitments or risks, allowing for immediate exploration. Enjoy the advantages of Apache Spark without vendor lock-in, supported by IBM's dedication to open-source technologies and extensive enterprise experience. With integrated Notebooks serving as a connector, the process of coding and analytics becomes more efficient, enabling you to focus more on delivering results and fostering innovation. Additionally, this managed Apache Spark service provides straightforward access to powerful machine learning libraries, alleviating the challenges, time investment, and risks traditionally associated with independently managing a Spark cluster. As a result, teams can prioritize their analytical goals and enhance their productivity significantly.
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.
API Access
Has API
API Access
Has API
Integrations
Apache Spark
Amazon EC2
Apache Cassandra
Apache HBase
Apache Hive
Apache Mesos
Hadoop
Java
Kubernetes
MapReduce
Integrations
Apache Spark
Amazon EC2
Apache Cassandra
Apache HBase
Apache Hive
Apache Mesos
Hadoop
Java
Kubernetes
MapReduce
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
IBM
Founded
1911
Country
United States
Website
www.ibm.com/analytics/ca/en/technology/cloud-data-services/spark-as-a-service/
Vendor Details
Company Name
Apache Software Foundation
Founded
1995
Country
United States
Website
spark.apache.org/mllib/
Product Features
Data Analysis
Data Discovery
Data Visualization
High Volume Processing
Predictive Analytics
Regression Analysis
Sentiment Analysis
Statistical Modeling
Text Analytics
Data Science
Access Control
Advanced Modeling
Audit Logs
Data Discovery
Data Ingestion
Data Preparation
Data Visualization
Model Deployment
Reports
Integration
Dashboard
ETL - Extract / Transform / Load
Metadata Management
Multiple Data Sources
Web Services
Product Features
Machine Learning
Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization