Self Service Enterprise AI

Data Science and Machine Learning Platform purpose-built for the Enterprise

Drag and Drop  |  Multi-tenant  |   Cloud-agnostic |  Scalable  |  Extensible  
 Advanced Analytics Studio 

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Data Science

Kickstart your AI Journey
with Sparkflows

Why Sparkflows ?

Drag-and-drop ML/AI

For Analyst, Data Scientist

and Data Engineers 

Product Highlights

TensorFlow 2.0, H2O,

Scikit-learn SparkML

Quick Links

FAQs, Pricing. Contact Us, Datasheet, Quickstart Guide

ML/AI at the Speed of Business

Collaboratively build best-in-class Machine Learning applications in hours using 300+ pre-built drag-and-drop processors in Sparkflows Big Data ML Workbench and make data driven decisions in real time



De-dupe, aggregate, join and clean data with 50+ data processors. Even bring in third-party data to enrich to generate deeper insights.


Identify features that affect the model or just do unsupervised learning with just few clicks.

Build Model

Choose the right model, train and test and choose the one that predicts more accurately. Re-train with more data for better results.


Deploy on the stack of your choice. Deploy via Docker image or otherwise on any of the cloud providers for ML model deployment.

Ingest data from wide variety of sources with our 25+ ready to use connectors. Don't find the one you need, build it yourself or ask us.

ML/AI for Everyone

Irrespective of the skillset, empower every Analyst, Citizen Data Scientist, Data Engineer and Data Scientist to power data-driven decisions with ease and efficiency

Data Analyst

No more waiting for Data engineers

to set things up. Incredibly easy to connect to any size datasets and get insights within a few hours. No big data skills required to build advanced ML models to predict customer behavior, churn, or revenue.

Data Scientist

Accelerate big data analytics and

build ML models 10x faster. Choose from 30+ ML nodes to build, train and test models. Re-train models faster with advanced scheduling features and distributed compute engine, and operationalize models with minimal effort.