# Overview Seclea's Python API, `seclea-ai`, provides seamless integration with any Python-based AI development workflow, including popular cloud-based development environments such as Google Colab. The API is easily accessible via PyPI, allowing you to quickly install and integrate `seclea-ai` into your projects by simply running `pip install seclea-ai`. With `seclea-ai`, you can effortlessly: 1. **Record your data:** The API enables you to log and track datasets used in your AI projects, ensuring complete traceability and accountability throughout the development process. 2. **Record data transformations:** `seclea-ai` allows you to document and monitor all data transformations applied to your datasets, including cleaning, feature engineering, normalization, and other preprocessing steps. This level of tracking ensures data integrity and facilitates the optimisation of your AI models. 3. **Record your models:** The Python API offers comprehensive model tracking, allowing you to log and manage model architectures, training configurations, and performance metrics. This feature streamlines model versioning and enables you to easily compare and evaluate different models throughout the development lifecycle. By utilizing the `seclea-ai` Python API, you can bring the power of the Seclea Platform to your Python-based AI projects, ensuring efficient project management, compliance, and traceability across your entire development workflow. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://documentations.seclea.com/seclea-user-documentation/python-api-seclea-ai/overview.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.