Solutions

Optimize programmatic trading and increase business velocity with full stack model observability:

Maximize margins and ROI with time series analysis and insights into areas where a model over or underperforms
Proactive monitors to automatically surface performance issues at scale
Instantly surface issues and retraining opportunities to deliver model improvements continuously

Maximize customer retention and engagement with your business:

Focus where to invest retention efforts by uncovering insights into how different dimensions impact the propensity to churn
Perform time series analysis of model performance to tease out new customer trends or correlations
Detect underperforming cohorts of predictions so you can optimize business tactics such as marketing campaigns more efficiently

Increase click through rate and improve overall model performance:

Proactively monitor new and unseen trends before they significantly impact model performance
Identify drifting features to actively improve models in production
Manage bad, corrupt, or missing key input data to easily retrain poor performing models

Detect model drift and maximize customer retention:

Automatically monitor for drift to account for costly model degradation
Proactively manage data quality metrics to ensure high quality training, validation, and production data
Understand your most important features with explainability to easily retrain models for variable timelines

Accurately predict demand to improve overall operational outcomes:

Catch drift in performance faster so you can better tune models and manage supplier/customer relationships
Map fluctuations in model performance back to the actual impact on business goals
Identify outlier events in regression models for more accurate predictions

Decrease fraudulent transactions, minimize model error, and monitor model performance:

Easily navigate misleading accuracy metrics to decrease financial loss
Monitor for drifting features, predictions, and actuals that result in model exploitation
Instantly map data quality with model performance to account for upstream data issues

Simplify monitoring and optimize highly complex model performance for continuous improvements:

Instantly detect underperforming features or dimensions to easily focus retraining efforts with performance tracing
Leverage bias tracing with explainability to prevent potential biases in how images are classified
Validate the quality of models, upstream data pipelines, and how your model responds to data changes prior to deploying

Eliminate underwriting guesswork and manage model drift to increase profitability:

Visualize drift between various model environments and versions to identify claims patterns
Clearly understand drift impact with drift over time widgets overlaid with your metric of choice
Analyze data drift at a granular level for future financial projections

Reduce credit decision risk for your business while improving access and opportunity for your customers:

Understand how your model weighs different features in its decisions with explainability tools
Quickly improve accuracy by exposing specific features or slices of predictions that perform better or worse
Broaden access to credit for your customers by evaluating other features and proxy metrics that have a bigger impact on repayment or default rates than traditional metrics like FICO scores

Gain a deeper understanding of your NLP models for improved model performance management:

Compare model performance of your language model for different cohorts to better focus data preparation or retraining efforts
Systematically catch gradual performance degradation and concept drift to effectively optimize outcomes
Trace bias throughout your model by leveraging key explainability metrics to detect potential biases in model outcomes

Increase sales by finding the optimal price point for your customers and business:

Increase model visibility within your organization by mapping your model’s decisions back to key business metrics
Compare how current pricing models perform against training, validation, or prior time periods
Instantly detect cohorts of underperforming predictions so you can optimize pricing decisions faster

Optimize model performance to maximize recommendation system model outcomes:

Proactively detect drift, data quality, and performance issues to ensure confidence with models in production
Visualize model performance at a granular level and automatically surface the first steps to model improvement
Identify the right time to retrain a model based on drifting features, predictions, and actuals

Maximize output and increase the predictability of returns for your business:

Perform time series analysis of model performance to tease out new trends or relationships
Gain a deeper understanding of the features and dimensions that affect forecasts with explainability comparisons
Surface slices of predictions that perform the best or worst for fast acting model visibility

Get ML observability in minutes.