Introduction

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:

• Getting-started is the fastest way to get started with Explainit
• Architecture describes Explainit's overall architecture.