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Average Ratings 0 Ratings

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ease
features
design
support

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Write a Review

Description

Daft is an advanced framework designed for ETL, analytics, and machine learning/artificial intelligence at scale, providing an intuitive Python dataframe API that surpasses Spark in both performance and user-friendliness. It integrates seamlessly with your ML/AI infrastructure through efficient zero-copy connections to essential Python libraries like Pytorch and Ray, and it enables the allocation of GPUs for model execution. Operating on a lightweight multithreaded backend, Daft starts by running locally, but when the capabilities of your machine are exceeded, it effortlessly transitions to an out-of-core setup on a distributed cluster. Additionally, Daft supports User-Defined Functions (UDFs) in columns, enabling the execution of intricate expressions and operations on Python objects with the necessary flexibility for advanced ML/AI tasks. Its ability to scale and adapt makes it a versatile choice for data processing and analysis in various environments.

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

Screenshots View All

Screenshots View All

Integrations

Apache Spark
Python
Amazon Web Services (AWS)
Apache Arrow
Apache Cassandra
Apache HBase
Apache Hive
Apache Mesos
Databricks
Delta Lake
Google Cloud Platform
JSON
Java
Kubernetes
Microsoft Azure
PyTorch
R
Rust
Scala
Unity Catalog

Integrations

Apache Spark
Python
Amazon Web Services (AWS)
Apache Arrow
Apache Cassandra
Apache HBase
Apache Hive
Apache Mesos
Databricks
Delta Lake
Google Cloud Platform
JSON
Java
Kubernetes
Microsoft Azure
PyTorch
R
Rust
Scala
Unity Catalog

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

Daft

Country

United States

Website

www.getdaft.io

Vendor Details

Company Name

Apache Software Foundation

Founded

1995

Country

United States

Website

spark.apache.org/mllib/

Product Features

Data Science

Access Control
Advanced Modeling
Audit Logs
Data Discovery
Data Ingestion
Data Preparation
Data Visualization
Model Deployment
Reports

Product Features

Machine Learning

Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization

Alternatives

Alternatives

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