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AIQC

             
  





  
  
  
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    Tabular(2D)
    Sequence(3D)
    Image(4D)
  
  
    Classification(binary, multi)
    
      
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    Quantification(regression)
    
      
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    Forecasting(multivariate)
    
      
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  AIQC's structured protocols automate the tedious pre-processing and post-processing steps that are unique to each type of data and analysis.


  This enables teams to stay focused on data science, as opposed to writing DIY software that manages the machine learning lifecycle and all of its edge cases.






  
  
  

  



  


  
     
     
     
     
  

  
    Train
    Validation
    Test
    Inference
  
  
    Prevent evaluation bias with 3-way+ stratification.
    Validate the structure of new samples.
  

  
    Prevent data leakage by only using preprocessing information derived from the training split/fold.
    Prevent data drift by using original preprocessors.
  

  
    Prevent overfitting by evaluating each split/ fold of every model
    Detect model rot by reevaluating with supervised datasets.
  

  
    Ensure reproducibility by using a standardized framework that records the entire workflow.
  







  


  Let's get started!
  
  
    → Use Cases & Tutorials
  











           