A faster path to better AI & ML performance monitoring

Our platform helps organizations of all types and sizes deliver better results for their machine learning models

Data scientists and machine learning engineers are often on the front lines of the most important challenges facing their organizations. From credit and insurance underwriting to retail demand and yield forecasting, Arize enables ML teams to accelerate model velocity and time to value across a range of use cases. Here are demos and resources by industry and use-case.

Algorithmic trading
Churn forecasting
Click-through rate (ad optimization)
Customer lifetime value
Demand forecasting
Fraud detection
Image classification
Insurance
Lending
Natural language processing
Price optimization
Recommendation systems
Yield forecasting
Algorithmic trading
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
Churn forecasting
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
Click-through rate (ad optimization)
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
Customer lifetime value
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
Demand forecasting
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
Fraud detection
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
Image classification
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
Insurance
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
Lending
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
Natural language processing
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
Price optimization
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
Recommendation systems
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
Yield forecasting
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

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