MILO-ML offers an on‑premise Auto‑ML platform that automates the entire machine‑learning workflow, delivering predictive analytics tools without requiring data‑science, software‑engineering, or programming expertise. The turnkey solution can be deployed within a company’s own infrastructure to address a wide range of research and business use cases, handling data preprocessing, model selection, training, and deployment automatically. It enables organizations to build and operationalize AI models quickly and securely, even when they lack in‑house ML talent.
Funding
Funding not disclosed
Founders
Product
Problem
Many organizations lack in‑house machine‑learning expertise, software‑engineering resources, or the ability to write code, yet they need predictive analytics for research and business decisions. Deploying cloud‑based AI solutions can also conflict with data‑privacy, security, and regulatory compliance requirements.
Solution
MILO delivers an on‑premise Auto‑ML platform that automates the entire model development workflow without requiring any machine‑learning knowledge, software engineering skills, or programming. Users upload their datasets, define the prediction goal, and the system automatically selects features, trains multiple algorithms, evaluates performance, and produces a fully trained model ready for local deployment. Because the solution runs entirely within the organization’s infrastructure, it satisfies strict data‑privacy and compliance mandates while providing rapid, repeatable analytics capabilities across diverse use cases.
Target Audience
Primary customers are enterprises, research institutions, and regulated industries (e.g., finance, healthcare, manufacturing) that need predictive analytics but lack dedicated data‑science teams and must keep data on‑site.
Features
- Fully automated end‑to‑end pipeline that handles data preprocessing, feature engineering, model selection, and hyperparameter tuning
- No‑code interface allowing business analysts to create predictive models by simply uploading data and specifying outcomes
- On‑premise deployment ensures all data and models remain within the organization’s secure environment
- Supports a wide range of supervised learning tasks (classification, regression) for both research and operational applications
- Generates ready‑to‑use model artifacts that can be integrated into existing business systems without additional coding