AKIVA Data Contracts: A Toolkit for Data Contract Management and Drift Detection in ML/LLM Pipelines

AKIVA Data Contracts is an open-source Python toolkit designed to address data quality challenges in ML/LLM pipelines. It provides data contract management and drift detection capabilities, supporting auto schema inference, data validation, statistical profiling, and CI/CD integration. This toolkit helps teams proactively monitor and maintain data quality in production environments, ensuring model performance stability.

In ML systems, data distribution changes (data drift) can degrade model performance even if code remains unchanged. For LLM applications like RAG, issues such as knowledge base updates, user input pattern shifts, and multi-modal data introduction often lead to hidden data quality problems, which are only detected after performance deterioration, causing business losses. AKIVA is built to solve these issues.

AKIVA offers three core capabilities:

1. Auto Schema Inference: Analyzes datasets to automatically identify field types, ranges, constraints, and statistical features for structured (numeric, categorical, time) and unstructured (text, embeddings) data.
2. Data Validation: Checks type correctness, null values, business rules (e.g., value ranges, category validity), and LLM prompt template variable integrity.
3. Statistical Profiling & Drift Detection: Collects data stats (histograms, correlations, missing value patterns) and detects drift by comparing new data with historical profiles (single/multi-variable distribution shifts, correlation changes).

AKIVA uses a layered architecture:

1. Contract Definition Layer: Declarative contracts (YAML/Python) include field-level (type, constraints) and dataset-level (row range, owner) metadata, supporting version control.
2. Execution Engine Layer: Plugin-based engine for validation/detection, supporting batch/stream processing and sampling for large datasets.
3. Integration Adaption Layer: Integrates with Pandas, Polars, MLflow, LangChain, LlamaIndex, and other ML/LLM frameworks.

AKIVA integrates with DevOps workflows:

1. Pre-Commit Hooks: Automatically validates data contracts on code submission, blocking invalid changes.
2. CI Pipeline: Runs regression tests to detect drift/quality degradation between versions.
3. Deployment Gate: Blocks deployment if data quality metrics fail thresholds.
4. Monitoring & Alerts: Tracks production data quality and sends alerts via email, Slack, or PagerDuty on drift detection.

AKIVA applies to all ML/LLM lifecycle stages:

• Data Prep: Defines feature contracts and infers schemas for new datasets.
• Training: Validates training data for label leakage and data shard consistency.
• Serving: Monitors input data drift and training-serving skew.
• LLM-Specific: Checks RAG knowledge base quality, prompt template validity, and multi-modal data quality.

AKIVA is open-source under Apache 2.0, with GitHub docs/examples. Community extensions include cloud integrations (AWS/GCP/Azure), migration guides for tools like Great Expectations, and industry-specific contract templates. Future plans: enhance real-time data handling, improve time-series drift detection, and add visualization reports.

AKIVA brings software contract principles to data governance, critical for ML/LLM system reliability. It enables systematic data quality management like code quality, making it a key component of modern MLOps toolchains as AI applications scale in production.