Predictive AI

Build more accurate models at scale.

ML Ready Data

Start modeling in minutes, not hours. Skip manual data prep. Clean, balance, and ensure your data is compliant and ready for feature engineering and modeling.

Data Healing

Start analysis and modeling without removing missing values.

Deduplication

Skip repetitive coding with repeatable recipes for duplicate removal.

Unbias your data

Detect and correct dataset oversampling and undersampling with synthetic data generation.

Rapid Experimentation

Simplified multimodal data preprocessing. Take the complexity out of processing different types of data for intricate ML use-cases. Select and execute the best data preprocessing approaches, and customize modeling pipelines and recipes.

Time aware data

Efficiently explore hundreds of lags, differencing options, and dataset augmentations to find approaches that will best improve model accuracy.

Geospatial data

Enhance limited GIS data with synthetic data generation. Explore optimal preprocessing methods to define features and transform data types.

Image data

Optimize image data processing with vectorization and editable feature engineering blueprints. Expand limited datasets using image transformation pipelines.

Categorical data

Optimally transform data into useful categories by unbiasing over- and under-sampled data with synthetic data generation. Select encoding approaches and the most relevant features.

Predictive Use Cases

Time series modeling. Customize out-of-the-box time series technique suggestions and generate high-quality, hyper-granular forecasts without the complexity of mastering time series modeling approaches.

Clustering and seasonality

Use a variety of approaches, such as Dynamic Time Warping (DTW), to find time-based patterns in similar series rather than business logic alone.

Cold start forecasting

Use various techniques that allow you to learn from similar series to forecast with limited data. Handle irregular data by automatically aggregating data or using row-based time series.

Nowcasting

Easily customize prediction windows to forecast current, potentially unknown values, properly treating current and historical values with our suite of time series models and techniques.

Time series anomaly detection

Detect unusual data patterns and understand the features driving them on an individual and holistic basis. Use Synthetic AUC to gauge the accuracy of various unsupervised approaches.

Applied Predictions

One-click deployment. Instantly generate an AI pipeline to build and deploy registered models into production without compromising integrity.

Enable self-service prediction exploration

Create shareable, interactive applications that empower business users to make data-driven decisions with just a few lines of code.

Power business systems with AI

Integrate predictions into your internal systems and applications with our open API. Use our monitoring tools to track model health and prediction latencies.

Shorten development with reusable application templates

Use our GUI app builder to create and customize predictive apps, or use existing templates for forecasting “what if” analysis, and more.

Model Integrity

Visualize model explainability and quality. Explore every facet of your model with interactive visualizations of feature impact, effects, coefficients, and more.

Feature importance

Understand which features are driving model decisions with Shapley values, LIME, Violin plots, and feature impact visualizations.

Bias detection

Detect underlying biases by generating predictions and segmenting results based on variables like age, race, and gender.

Model coefficients

Make models fully explainable by visualizing how each component contributes to the final prediction.

Feature effects

Improve your model by uncovering non-linear relationships, spotting impactful but low-relevance features, and identifying potential errors.