Platform designed to manage end to end data challenge
Fully Managed Platform with Flexible Hosting Options
Our trustii.io platform is available from any location and requires no installation, maintenance, or software training. With flexible hosting options and the ability to choose data scientists based on university and region, you can maintain control over your data.
Automated Machine Learning based on Open Source
Trustii.io utilizes the best open source automated machine learning libraries and combines them using ensembling methods to create high-quality models for demonstrations and proof of concepts. Our automated machine learning model is also a common choice as a baseline in competitions.
Machine Learning Challenges
In addition to using automated machine learning for demos and proof of concepts, Trustii.io hosts competitions where student data scientists from top universities compete to enhance the quality of the automated model. This enables organizations to obtain a model that meets their specific needs.
Future proof data scientists
Our platform features a real-time leaderboard that allows organizations to identify the top solutions and talented students, enabling them to reach out and potentially hire them for further collaboration within their company.
Trustii.io's integrated forum and team collaboration feature make it easy for data scientists and organizations to communicate and work together during data challenges.
Integrated Jupypter Notebook
Trustii.io offers the option of hosting a Jupyter notebook for data scientists, providing an easy way to develop and evaluate machine learning models while maintaining data confidentiality.
Model delivery and deployment
With trustii.io, you have the option to deploy your trained model in the cloud and make predictions on live data using our user interface or API. Alternatively, you can download the model and deploy it on your own cloud platform. The model is provided as a notebook with the source code included.
At Trustii.io, we have developed our own machine learning ensembling methods to improve the performance and efficiency of machine learning models. Our ensemble approach helps to reduce the variance of the model, leading to more accurate predictions.
We believe that every model counts in a challenge, not just the top three solutions.