Automated Machine Learning
Tellius puts machine learning in the hands of analysts and citizen data scientists with visual and explainable automated ML modeling.
Making ML/AI Accessible with Automated & Visual Machine Learning
OLD WAY IS COMPLEX
- Machine learning tools and models are difficult for business users to understand
- Open source tools required skilled resources to implement and scale
- ML is not integrated into the analytics workflow; model outputs must be exported to viz tools
TELLIUS SIMPLIFIES ML FOR BUSINESS ANALYSIS
- Visualize machine learning model outputs to easily interpret and explain results
- Unified platform for data prep, insights discovery, visual exploration, ML modeling - all intelligently powered by AI
- Custom ML modeling accessible to citizen data scientists and advanced analysts
Choice of Automated or Custom ML Modeling
Apply sophisticated machine learning models to your data in your preferred approach – via single-click AutoML (everything handled for you); via Point & Click Mode for full feature/ model/ hyperparameter selection granularity; or import your own custom coded algorithms.
Explainable AutoML You Can Visualize
Automated machine learning serves up deep insights from complex data that business users and data experts alike can understand and trust to power decisions. Best fit models are finally presented to the users in standard LIME methodology to explain model performance and metrics.
Flexibility to Use Custom Python & Spark Code
Take control of model building and bring your own Python and Spark code to the platform for maximum flexibility and customization. Model results are served with the same level of explainability and narrative as the AutoML process.
Seamlessly Integrate ML Insights into Business Workflow
Share Visual Insights with Business Users
- Share AI-powered insights and predictions throughout the business collaboratively via Vizpads, mobile apps, APIs, or embedded inside your applications.
Automate the Analytics Lifecycle
- Automate data prep, feature engineering, modeling, and deployment — while receiving proactive insights as data changes — freeing up analytical resources for the next opportunity.