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Average Ratings 0 Ratings

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ease
features
design
support

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Write a Review

Description

Auraa is Covasant's innovative, agent-driven data platform designed specifically for Databricks, offering the quickest route to transforming data into AI-ready formats. By utilizing conversational AI features that operate in natural language, businesses can leverage agents to autonomously identify various data sources, construct pipelines, maintain data quality, and register all components in Unity Catalog right from the start. This approach completely removes the need for traditional pipeline code, significantly reduces engineering backlogs, and eliminates months of manual setup efforts. Typically, establishing a data lake on Databricks can take upwards of 18 to 24 months, but with Auraa, the onboarding of the initial data source can be accomplished in less than 15 minutes, the first use case can be launched within hours, and the entire deployment period can be condensed to approximately 8 to 10 weeks, resulting in a cost reduction of up to 70%. Auraa redefines data engineering decisions by managing them as structured, versioned, and governed metadata instead of relying on fragile, hand-coded pipelines. The platform guarantees that the Databricks lakehouse is not only reproducible and auditable but also consistently enhances its capabilities through the use of agents, paving the way for continuous improvement and efficiency in data management.

Description

Originally created by Uber, Horovod aims to simplify and accelerate the process of distributed deep learning, significantly reducing model training durations from several days or weeks to mere hours or even minutes. By utilizing Horovod, users can effortlessly scale their existing training scripts to leverage the power of hundreds of GPUs with just a few lines of Python code. It offers flexibility for deployment, as it can be installed on local servers or seamlessly operated in various cloud environments such as AWS, Azure, and Databricks. In addition, Horovod is compatible with Apache Spark, allowing a cohesive integration of data processing and model training into one streamlined pipeline. Once set up, the infrastructure provided by Horovod supports model training across any framework, facilitating easy transitions between TensorFlow, PyTorch, MXNet, and potential future frameworks as the landscape of machine learning technologies continues to progress. This adaptability ensures that users can keep pace with the rapid advancements in the field without being locked into a single technology.

API Access

Has API No 

API Access

Has API No 

Screenshots View All

No images available

Screenshots View All

Integrations

Amazon Web Services (AWS) No 
Azure Databricks No 
Databricks Yes 
Flyte No 
Keras No 
MXNet No 
Microsoft Azure No 
PyTorch No 
Python No 
TensorFlow No 

Integrations

Amazon Web Services (AWS) Yes 
Azure Databricks Yes 
Databricks No 
Flyte Yes 
Keras Yes 
MXNet Yes 
Microsoft Azure Yes 
PyTorch Yes 
Python Yes 
TensorFlow Yes 

Pricing Details

No price information available.
Free Trial Yes 
Free Version Yes 

Pricing Details

Free
Free Trial No 
Free Version Yes 

Deployment

Web-Based Yes 
On-Premises Yes 
iPhone App Yes 
iPad App Yes 
Android App Yes 
Windows Yes 
Mac Yes 
Linux Yes 
Chromebook Yes 

Deployment

Web-Based Yes 
On-Premises Yes 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours Yes 
Live Rep (24/7) Yes 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) Yes 
In Person Yes 

Types of Training

Training Docs No 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Covasant Technologies Private Limited

Country

India

Website

www.covasant.com

Vendor Details

Company Name

Horovod

Website

horovod.ai/

Product Features

Product Features

Deep Learning

Convolutional Neural Networks No 
Document Classification No 
Image Segmentation No 
ML Algorithm Library No 
Model Training No 
Neural Network Modeling No 
Self-Learning No 
Visualization No 

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