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Hybrid ML Research &
Recommendation Platform

Our Hybrid ML Platform is designed specifically for financial experts and organizations who want to make informed decisions based on accurate predictions with clear explanations, making them easier for human experts to understand and interpret.
The easy-to-understand models produced by our platform offer a unique solution for financial predictions by combining human expertise and ML algorithms to deliver accurate and interpretable actionable insights.
The platform is tailored specifically to the finance industry and utilizes data and ML techniques optimized for financial prediction problems.
The user-friendly design, real-time updates, and ongoing signals make this product an attractive option to craft and design a custom-made actionable insight dataset.
By continuously improving the product through ongoing development and collaboration with industry experts, the Hybrid ML Platform is poised to become a leading solution in the financial prediction industry.

Key Functionality:

  1. Model Building – Users have the option to choose the models they want to build based on their specific requirements.
  2. Interval – Users can choose the interval for the model’s predictions (Daily, Weekly, Monthly)
  3. Features – Users can select the features they want the model to consider when making predictions
  4. Prediction Target – Users can decide which prediction target they want the model to predict, such as stock prices or trading volume
  5. Feature Importance – The model provides information on the importance of each feature used in its predictions.
  6. Sheets-Based Delivery – The user can observe the model’s learning process in real-time using a Google Sheets file sent to their email.
  7. Design – Easy to build and train a new active actionable insight dataset

Coming Soon

  1. Text-Based Explanation Report – The model generates a report with clear explanations for its predictions using advanced language generation techniques. This unique feature enables a better understanding of the reasoning behind the model’s predictions
  2. Alternative Data Instant Feedback – Users have the ability to combine alternative data with traditional data in a trained model to get near-instantaneous feedback if it has value on the model

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