AI Platform: From Notebook to Production, Faster
An ML model in a notebook is an asset; a model in production is a competitive advantage. The Arkham AI Platform is engineered to bridge that gap. It is a comprehensive, end-to-end environment designed to manage the full lifecycle of machine learning, streamlining the path from experimentation to operational impact.
Our platform is built on a philosophy of "pro-code" flexibility with low-code options. It gives builders programmatic control within a powerful notebook environment while providing UI-driven tools for rapid visualization, monitoring, scheduling, and consumption, all supercharged by TARS an onboard AI co-pilot.
ML Hub Anomaly Detection Notebook with TARS UI
Our AI Platform provides a unified control plane for the entire machine learning lifecycle, structured around three key pillars:
- Model Development: The ML Hub provides a "pro-code" notebook environment where data scientists can explore data, experiment with features, and train models using familiar Python libraries. It combines the flexibility of custom code with the acceleration of our integrated AutoML framework.
- Model Management & Deployment: A versioned Model Registry is a single source of truth for all model artifacts and performance metrics. From the registry, models can be seamlessly operationalized as either a scheduled batch inference job or a live API endpoint.
- Model Consumption & Monitoring: Model outputs, whether batch or real-time, are first-class citizens in the Arkham ecosystem. They can be explored in the Data Catalog, analyzed in Workbooks, or used to power operational applications, closing the loop on the MLOps lifecycle.
Core Components
Our AI Platform is comprised of several integrated services that work together to support the ML lifecycle:
- ML Hub: A powerful, Python-based notebook interface to build, train, and deploy machine learning models.
- Workbooks: An interactive dashboarding tool for creating analytical apps and reports on top of Lakehouse data or model results.
- TARS: A platform-wide conversational co-pilot that accelerates workflows by generating code, answering questions, and executing tasks.
Core Concepts
Concept
Description
Modelo
A trained machine learning algorithm that can be versioned and deployed.
Model Version
An immutable, timestamped snapshot of a model, including its code, parameters, and artifacts.
Inference Job
A scheduled execution of a model that runs on new data and publishes its predictions as a new dataset.
API Endpoint
A live, secure endpoint that serves real-time predictions from a deployed model.
Workbook
An interactive dashboard used to visualize model outputs, monitor performance, or analyze results.
The Builder's Workflow: From Development to Impact
The diagram below outlines the end-to-end process of developing and deploying a model using our Arkham AI Platform. It illustrates how the components coordinate to create a seamless workflow from initial development to operational impact.
Related Capabilities
The AI Platform is deeply integrated with the other core capabilities of the Arkham ecosystem.
- Data Platform: Provides the high-quality, production-grade datasets that serve as the essential fuel for all model training and batch inference jobs.
- Ontology: Delivers semantically rich, business-aware features to models and provides governed metrics for consumption in Workbooks.
- Governance: All models, datasets, and jobs within the AI Platform are organized and secured by the rules of their parent Project.
