Why Visual ML
Model building requires many skills. It doesn’t matter if you know how to code or not. What is important is that you deeply understand the problem you are trying to solve.
Our platform allows you to click your way through creating an end-to-end ML and AI application, from preprocessing data to deploying a model to production via an API or a service layer. We empower you with a simple, powerful and automated system to handle all tasks in the analytics development life cycle.
All model development and deployment processes are automatically documented and the code is automatically generated. You can edit and customize if you wish.
The simplicity of Visual ML appeals to data scientists, data and business analysts, AI developers, and executives alike.
The 7 steps in using Visual ML.
1 Prepare data
Connect to or upload a dataset, explore it visually and interact with it. Click to apply various algorithms to preprocess, prepare and define the data for modeling.
The value is exponential.
Creates and administers collaboration between teams so that your organization can put cutting-edge technology to work on real issues that matter to your customers.
Automated AI pipeline increases productivity exponentially by providing modules that can be leveraged in building and integrating AI applications into business processes faster and efficiently.
Centralizes and standardizes AI capabilities and products across your organization. Algorithms, pre-existing and new, are managed centrally, making it easy to embed AI products into your business processes and applications.
We break the black box open by making models completely transparent. Your organization can establish accountability, explainability, and fairness by automated documentation and validation.