Bridge AI/ML with Business Intelligence Programs

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AtScale forms a bridge between AI and BI while establishing the foundation for simplifying AI/ML pipelines and accelerating feature engineering by data scientists.

Build a Common Data Language

The AtScale semantic layer can establish a common definition of metrics and dimensions for data science and business intelligence teams.

Bridge AI and ML With Business Intelligence

Eliminate inconsistency and duplicative work that happens when data science and business intelligence teams look at data differently.

A single source of governed analytics, that can be continually updated and managed as the business evolves, creates trust in data and fosters a culture of collaboration across all data consumers.

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Simplify Feature Engineering

AtScale includes a powerful feature design utility that supports both code-based and visual data modeling. Data teams can collaborate closely with business users on engineering features defined on top of raw cloud data.

Transform numerical fields to categoricals. Build custom-calculated fields. Easily create time-relative features with a full range of lags. Feature engineering at the semantic layer is dramatically simpler than physical transformations, as it exists purely as a logical definition.

Harden AI/ML Data Pipelines

AtScale serves features on demand by leveraging a powerful query virtualization platform, meaning data is not physically moved into the AtScale platform. Once a feature is published, AtScale can dynamically generate queries against source data based on the logical feature definition, allowing downstream pipelines to consume features from the semantic layer.

Leveraging AI-Link, features can be leveraged and bi-directionally managed through Python scripts. This approach radically simplifies AI/ML data pipelines while hardening against disruption caused by changes to underlying feature definition.

Simplify Data Pipelines
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Publish Model Results to Existing Dashboards

Machine Learning Model-generated insights, like predictions, can be published back to cloud data platforms and the semantic model. This approach lets modeled insights integrate with the same semantic structure the business is already utilizing. Thus, predictions can easily be consumed with the same BI platforms already in use by the business

By leveraging dimensionality established by BI teams, decision makers can more confidently navigate large predicted data sets — using the same time, product, and geographical hierarchies to analyze predictions as they would actual data.

Integrate Directly with Jupyter, AutoML, and Automation Platforms

AI-Link connects the AtScale semantic layer to a broad range of data science tools through its programmable Python API.

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Bridge Ai Ml With Business Intelligence

Live Connection to Cloud Data Platforms

Leverage the full power of your cloud data infrastructure to address the needs of your data science and AI/ML programs — without time-consuming data engineering and complex data pipelines.

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Get access to free semantic layer reports, webinars, videos ands much more.

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On-Demand Webinar

How to Accelerate AI and BI Business Impact with an Effective Data Strategy

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On-Demand Webinar

How to Align AI & BI to Business Outcomes

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On-Demand Webinar

How to Bridge Data Science and Business Intelligence

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