AI Platform: An Architecture for the ML Lifecycle

The Arkham AI Platform is a comprehensive, end-to-end environment designed to manage the full lifecycle of machine learning. It provides builders with a cohesive set of tools to accelerate development, ensure reproducibility, and streamline the path from experimentation to production.

The platform is built on a philosophy of "pro-code" flexibility, offering programmatic control via Python SDKs while leveraging UI-driven components for visualization and consumption.

Core Components

The AI Platform is comprised of several integrated services that work together to support the ML lifecycle:

• AutoML Framework: A code-first framework accessed via the `ArkhamPy` SDK. It uses a library of transparent Model Classes to rapidly generate high-performing baseline models for common tasks like forecasting and classification.
• Workbooks: An interactive dashboarding tool for creating analytical apps and reports on top of Lakehouse data or model results. It provides a low-code interface for business users to consume the data products you build.
• AI Assistant (TARS): A platform-wide conversational interface that uses an underlying Ontology to translate natural language questions into executable queries. For builders, it's a powerful tool for rapid data exploration and validation.

How It Accelerates the ML Lifecycle

The diagram below outlines the end-to-end process of developing and deploying a model using the Arkham AI Platform. It illustrates how the components work in concert to create a seamless workflow.

This integrated architecture provides key technical advantages:

• Seamless Data Integration: The platform connects directly to the Data Lakehouse, meaning models are trained on governed, high-quality data from the Gold layer without needing to build separate data extracts.
• Reproducibility by Design: Every component, from the development environment in the ML Hub to the final deployed model, is versioned. The Model Registry acts as the central source of truth for all model artifacts, parameters, and code.
• Unified MLOps: The platform provides a single, consistent path to production. Whether a model is generated via AutoML or is a fully custom build, it follows the same deployment and monitoring workflow, simplifying operations.
• Flexible Workflows: Builders can choose the right tool for the job. Use the AutoML Framework for rapid baselining, dive into a custom notebook for deep research, and present results in an interactive **Workbook**—all within the same environment.