TigerML Foundations | Tiger Analytics

Data exploration workbench

A lightweight UI-driven tool to jump-start your data exploration process. This includes data health checks, univariate & bivariate analysis, data imputation, visual exploration, key driver analyses, etc.
Create comprehensive… analytical reports on data across different dimensions in a no/low code mode. Sharing of outputs via HTML reports, spreadsheets, pdf documents, etc.
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Feature engineering

Accelerate your data transformation and feature engineering efforts. Use optimized, extensible Pyspark libraries for low-code feature engineering. Leverage our feature kit to create reusable pipelines to integrate with your feature… store.
Pre-defined recommendations for relevant transformations and features across various domains and industries.
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Modeling workbench

Accelerate model development with ready-to-use foundational components
Solution templates: Pre-built solution templates for common ML use cases in marketing, pricing, customer analytics etc. These are… pre-organized working directories, notebooks, deployment frameworks, and documentation extensions to build, customize, and deploy data science projects easily.
Auto ML: Use our genetic algorithm-based optimizer for searching the best models and, subsequently, hyperparameter tuning. The leaderboard of best-performing models from hundreds of pre-built pipelines helps guide the model search and selection process.
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Model evaluation workbench

Toolkits to address model explainability and ensure continuous governance.
Evaluation reports: Evaluate the efficacy of a model’s performance with standard model metrics and interpretations. Perform a… side-by-side assessment of different models and their execution.
Explainability reports: Understand and visualize the contribution of various features towards the end outcome. Depending on the use case, explainability is as important as accuracy.
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Deployment and monitoring

Toolkits to address model explainability and ensure continuous governance.
Deployment pipelines: Scale model production through composable deployment pipelines.
Monitoring and retraining: Configure… monitors and threshold-based triggers for data drift observability, model performance drift, and retraining pipelines.
Documentation templates: Ensure project success, collaboration, and adoption with Tiger’s documentation templates and best practices for end-to-end API and solution documentation in HTML format.
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Accelerate the model-to-value journey through TigerML toolkits

TigerML workbench features

Why choose us?

Accelerate the model-to-value journey through TigerML toolkits

TigerML workbench features

Why choose us?

Thank you!

Thank you!