AutoML
Automates the process of building ML models, from feature engineering to hyper-parameter tuning.
Automate predictive intelligence. Eliminate the analytics bottleneck.
PredictSense is the enterprise AutoML platform that ingests data, trains models on a live leaderboard, and deploys production-ready predictions instantly - securely on your infrastructure.
- Industry Agnostic
- Zero Data Exfiltration
- Automated Model Leaderboard
Platform Offerings
Six pillars, one workspace.
MLOps
Manage end-to-end ML pipelines, from training through production monitoring.
ML project management
Keep datasets, experiments and models organized in one shared workspace.
Explainable AI
Get a visual explanation for every model prediction, not just a score.
Click and Code
Start in a no-code UI, then drop into notebooks when you need full control.
Automated deployment and monitoring
Ship a model as a container in one click, then track it in production automatically.
How PredictSense works
From raw data to a deployed model, in five steps.
Connect data from any source, structured or unstructured, directly into PredictSense.
Prep the data automatically, then choose the variable you want to predict.
Generate and compare multiple models at once from 100+ algorithms.
Evaluate every model on a shared leaderboard — accuracy, bias and explainability side by side.
Deploy the best model as a container, a standalone app, or an API, in one click.
Under the hood
Every stage of the ML lifecycle, in one workspace.
Data preparation
- Connect to any structured or unstructured source
- Handle missing values and outliers with built-in EDA
- Extract and transform PDFs, HTML and other formats
- Schedule pulls to keep data current
- Drop into notebooks for custom prep
Automatic model training
- Choose from 100+ algorithms
- Automated feature engineering
- Hyper-parameter tuning, no manual search
Evaluation on every criterion
- Compare models on a shared leaderboard
- Industry-standard metrics: accuracy, bias, ROC-AUC
- Performance plots and confusion matrices
- What-if predictions by varying input features
One-click deployment
- Dockerized toolkit deploys to any production environment
- Data-drift detection compares live data to training data
- Re-training strategies keep the model current
Seamless collaboration
- Shared project management for data and business teams
- Pre-defined project templates
- Import and export projects
- Secure, governed MLOps environment
Who it's for
One platform, four ways to use it.
Banking
End-to-end AI for underwriting, fraud and decisioning — AutoML speeds up predictive analytics and automates decisions across banking workflows.
ML Teams
Work in a Python and Jupyter-based interface, build production-quality models, then deploy and monitor them without leaving the platform.
BI Teams
Build and compare multiple models through a visual, drag-and-drop interface, then push the best one to production in a click.
Project Managers
Keep data and business teams productive. Reuse, re-share and export models, and govern the ML pipeline securely from one place.
By the numbers