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Build & Operationalize Machine Learning models

Learn how to leverage machine learning for a competitive advantage

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Streamline the end-to-end ML lifecycle

Use Gathr’s self-service, visual canvas to easily build, train, and deploy complex models. Select from the platform’s 300+ built-in operators to create models faster than ever before.

Import from your favorite tools

Import ML models from platforms like TensorFlow and Scikit-learn. Get complete support for multiple algorithms for classification, clustering, and regression analysis.

Train and score models efficiently

Train models on curated datasets and build streaming pipelines for model scoring in real-time. Optimize model hyper-parameters to maximize performance.

Get deployment flexibility

Easily distribute your models on a Spark cluster for linear scalability. You can also use Kubernetes to scale models that you want to expose as a REST endpoint.

Leverage model-as-a-service

Once built, migrate your stand-alone data science models to an application exposed as a REST service for use across teams and use cases.


Anomaly Detection with Machine Learning at Scale

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Gathr Overview

One-of-a-kind no-code, unified
data-to-outcome platform

  • No-code for data at scale, batch and streaming
  • ML-powered apps and pipelines
  • 100+ business apps,
    250+ connectors,
    200+ operators
  • Unified collaborative experience
  • Best of open source and enterprise grade
  • Production ready output from day 1


Data integration

Machine learning

Actionable insights

Process automation

Business Apps

  • FinOps
  • CloudOps
  • DevOps
  • SecOps
  • ALM
  • Marketing
  • HR
  • Support

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