Why Sparkflows ?

Drag-And-drop ML/AI for Analyst, Data Scientist and Data Engineers

Data Science

Kickstart your AI Journey
with Sparkflows

TensorFlow 2.0, H2O,

Scikit learn, SparkML

Quick Links

Quickstart Guide, User Guide & Tutorials

Load

Enrich

Feature Engineering

Build Model / AutoML

Deploy

Ingest data from wide variety of sources with our 35+ ready to use connectors.

Don't find the one you need, build it yourself or ask us.

Dedup, aggregate, join and clean data with 80+ data processors. Even bring in third-party data to enrich and generate deeper insights.

Identify features that affect the model or just do unsupervised learning with just few clicks. needs to be polished. Most ideal would be Create new features to feed into the training pipeline with just a few clicks

Train models either using autoML capabilities of Sparkflows or choosing individual Algorithms. Evaluate the model using various metrics available in Sparkflows on validation/test data. Retrain the pipeline by click of few clicks after tweaking features, parameters until one arrives at a good model.

Deploy in the Environment of your choice - via Docker image, in a custom MLOps environment, any of the cloud providers.

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. Use AutoML to find the best models.

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 operationalise models with minimal effort.

Radically reduce the backlog of ETL and data orchestration tasks and devote more time to strategic projects. Build reusable processors and workflows, that anyone in the company can use for themselves to access datasets and run analytics.