
Model Governance Report
Enterprise Ready Platforms
01
Machine Learning Competitions
Predictive Analysis Competitions
Auto ML Competitions
Reinforcement Learning Competitions
02
Model Development
Python, R & Julia development tools
Scalable Docker based solutions
Active Directory integrated
03
Model Insights Platform
Model Inventory
Model Governance on Concept Drift & Population Stability
Model Monitoring & Deployment
Enterprise Ready Platforms
01
Machine Learning Competitions
Predictive Analysis Competitions
Auto ML Competitions
Reinforcement Learning Competitions
02
Model Development
Python, R & Julia development tools
Scalable Docker based solutions
Active Directory integrated
03
Model Insights Platform
Model Inventory
Model Governance on Concept Drift & Population Stability
Model Monitoring & Deployment
Meet Hexagon ML

OUR STORY
We are in a digital era where we have all data and storage needed to process information. However we are limited with time. Today, every industry's goal should be to improve their competitive capability and maximize information from data. We believe every company has the potential to achieve this. Our platforms enable and engage company's employees to participate and improve upon business predictive goals, whether its predicting or forecasting the sales for next year or churn rate.

OUR VISION
Our vision is to provide a flexible data science platforms that are secure, scalable and extendable. Data Science is an evolving field with variety of different types of problems - Forecasting, Predicting, Clustering, NLP, etc. We want to provide a platform that can grow to your needs.

TECHNOLOGY
We use python Django as our core framework. Our framework can be easily extended by adding new modules to Django. We provide Active Directory security. We integrate with most of the open source frameworks for metrics development and utilization for scoring.

IBM Research uses Hexagon-ml to streamline their competition experience
In 2019, Hexagon-ml helped IBM Research launch one of the first Reinforcement Learning competitions.
Thanks to Hexagon-ml's structured approach and well-defined processes, a total of 250+ teams and 735 submissions were recorded. We aim to continue this success and look forward to hosting the NeuroIPS Reinforcement Learning challenge in December 2022.
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default model text, and a search for 'lorem ipsum' will uncover many web sites still in their infancy. Various versions have evolved over the years, sometimes by accident, sometimes on purpose (injected humour and the lik