Accelerate Research with Deep Learning
An easy-to-use API & UI for the end-to-end AI lifecycle
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Reduce data wrangling with a declarative API
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Write 90% less glue-code with declarative pipelines
| Tabular(2D) | Sequence(3D) | Image(4D) | |
| Classification(binary, multi) | ✓ | ✓ | ✓ |
| Quantification(regression) | ✓ | ✓ | ✓ |
| Forecasting(multivariate) | ✓ | ✓ | ✓ |
AIQC provides structured protocols that automate data wrangling processes that vary based on: analysis type (e.g. categorize, quantify, generate), data type (e.g. spreadsheet, sequence, image), and data dimensionality (e.g. timepoints per sample).
The DIY approach of patching together custom code and toolsets for each analysis is not maintainable because it places a skillset burden of both data science and software engineering upon a research team.
How do you quality control (QC) your machine learning lifecycle?
| 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. | |||
Automated visualizations for each split & fold of every model
AIQC
Conduct what-if analysis to simulate virtual outcomes
Let's get started!
→ Use Cases & Tutorials