Clouderizer | End-to-End MLOps Platform

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  • Deploy, Score and Manage your ML models, without breaking a sweat
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Sentiment Analysis

Sentiment Analysis using PyTorch. Type in any text to measure its sentiment.

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Bank Marketing

Help Bank Marketing tem decide potential targets for next Term insurance marketing campaign.

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Heart Failure Prediction

Predict heart failure using Random Forrest using ScikitLearn.

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Deploy & Consume pre-packaged Models

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End-to-End MLOps Platform with Continuous Training

Deploy Any Model, Anywhere
  • Drag-n-Drop and Deploy models packaged as ONNX, PMML, MOJO
  • Deploy AutoML models from H2O.ai, KNIME, RapidMiner
  • Deploy in-house models developed using Tensorflow, PyTorch, Fast.ai, Sklearn, XGM
  • Deploy on GCP, AWS, Azure
  • Deploy on local Mac, Ubuntu
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Autogenerated Scoring UI
  • Data science teams can deploy and test their ML models easily
  • Cool way to showcase Machine Learning models to customers and leadership
  • Easiest way of getting ground truth feedback about models from internal team and beta testers
  • Works with PC, Smartphones, tablets
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Monitor Model performance
  • See how your models are doing in production using Ground Truth feedback from testers and internal users
  • Compare model performance and evaluation time.
  • Run analytics on results, user feedback, response time, input trends
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Code-less deployment
  • Deploy models procured from external vendors, packaged as ONNX, PMML, MOJO
  • Drag and Drop AutoML models from H2O, KNIME, RapidMiner
  • Zero lines of code needed. Deploy in 1 click.
  • No Software / IT / DevOps team needed.
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Code friendly deployment
  • Drag-n-Drop pre-built Python models in pickle format
  • Write your custom prediction, pre-processing and post-processing code
  • Deploy in 1 click
  • No Software / IT / DevOps team needed.
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Enterprise Ready
  • Setup end-to-end MLOps pipeline with Continuous Training
  • SaaS / OnPremise offering
  • Bring Your Own Cloud - Keep your models and data private
  • User management
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Documentation

Product Documentation to get you started and fill in with advanced features.

Tutorials

How To videos around core features

We used Clouderizer while teaching the Deep Learning course for graduate and undergraduate students, as one of the options to do programming homeworks and assignments at Georgia Tech.

With Clouderizer, students who used it found it easy to use, were able to get started with cloud GPU setup and running in few minutes, without hassle and extra work of setting up the environment, installing different packages, versions and dependencies.
I would recommend  Clouderizer to any university or company which runs employee / student training related to Machine Learning and is looking for a Cloud interface vendor / provider.

Sreenivasan AC
Graduate Teaching Assistant, Deep Learning, Georgia Institute of Technology

Clouderizer has saved me hours of low-level technical fiddling. I love how I can choose with one click which cloud service to spin up a machine on and how everything seamlessly backs up to my Google Drive. Customer service is excellent too - I had a connectivity issue and within ten minutes I had a video call and the problem was resolved. It’s well worth the money and highly recommended!

Anthony Holmes
CEO, Holmes Consulting

I have been using Clouderizer for over 3 months, its great to start because it lets me connect to Colab and GCP, perfect for starting out and you don’t want to fight with SSH

Jeremy Easterbrook
CEO Index
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