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What is Explainit?​

Explainit is a modern, enterprise-ready business intelligence web application that re-uses existing frameworks to manage and serve dashboard features to machine learning project lifecycle.

Explainit allows ML platform teams to:

  • Analyze Drift in the existing data stack (Features & Targets).
  • Prepare very short summary of productionized data.
  • Perform Quality Checks on the data.
  • Analyze relationship between features & target.
  • Understand more about intricasies of features and target.

Who is Explainit for?​

Explainit helps ML platform teams with DevOps experience monitor productionized batch data. Explainit can also help these teams build towards a explainability/monitoring platform that improves collaboration between engineers and data scientists.

Explainit is likely not the right tool if you:

  • Are in an organization that’s just getting started with ML and is not yet sure what the business impact of ML is.
  • Rely primarily on unstructured data.

What Explainit is not?​

Explainit is not:​

  • A BI / ETL / ELT system: Explainit is not (and does not plan to become) a general purpose data transformation or pipelining system. Users often leverage tools like dbt to manage upstream data transformations.
  • A data orchestration tool: Explainit does not manage or orchestrate complex workflow DAGs. It relies on upstream data pipelines to produce feature values and integrations with tools like Airflow to make features consistently available.
  • A dashboard engine: Explainit is not a replacement for your data dashboard engine or the source of truth for all dashboarding system in your organization. Rather, Explainit is a light-weight downstream layer that can monitor data from an existing batch data warehouse (or other data sources) in production.
  • A real-time dashboard: Explainit is not a real-time dashboard, but helps monitor data stored in batch systems (e.g. local) to make features & target consistently checks at production.

Explainit does not fully solve:​

  • Data quality / drift detection: Explainit is not complete solution built to solve data drift / data quality issues. This requires more sophisticated monitoring across data pipelines, served feature values, labels, and model versions.
  • Statistical tests: Explainit does not cover all statistical tests available yet, but cover few of them.
  • reproducible model explainability / data quality testing / model backtesting / experiment management.
  • Batch + real-time support: Explainit primarily processes already transformed feature values. Users usually integrate Explainit with batch systems (e.g. existing ETL/ELT pipelines).
  • native real-time feature integration.

How can I get started?​

The best way to learn Explainit is to use it. Head over to our Getting-started and try it out! {% endhint %}

Explore the following resources to get started with Explainit: