Simplify Data Pipelines and Get More from AutoML

The Data Dilemma for AutoML

AutoML platforms have revolutionized data science by making simplifying AI/ML model discovery and creation. Leading organizations are racing to get value from their enterprise AI investments and turning to this new class of technologies to accelerate their initiatives.

Enterprise AI runs on data and poses a new set of challenges for data teams already under pressure to satisfy needs of traditional business intelligence and analytics workloads. Not only do AutoML platforms consume massive amounts of data, they also generate new data that needs to managed and delivered to the hands of decision makers.

The Solution

AtScale’s semantic layer platform delivers data to AutoML platforms – simplifying pipelines and supporting feature discovery. Data teams define views of live cloud data that are optimized for model ingest including creation of calculated metrics, time- relative metrics, and custom dimensions. By employing virtualized views of data, data movement and ETL are minimized. Furthermore, pipelines are protected form changes to underlying data.

The semantic layer can also provide a path for publishing model results back to the business for consumption in existing dashboards and reports

AtScale helps data teams simplify and harden ML data pipelines while providing a path to publish model outputs back to the business.

The AtScale Advantage

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Semantic Layer

Establish single view of critical business metrics (e.g. revenue, COGS, headcount) and analysis dimensions, establishing a common analytics vocabulary across all data consumers. Blend data from broader range of internal sources and 3rd party data to expand universe of features.

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Support Time Series Analysis

Maintain curated set of time-relative measures with no complex SQL. Automatically create time series features based on your definitions of time.

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Feature Engineering

Deliver comprehensive view of all variables with simplified transformations and minimal data engineering to feed models.

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ML Model and AutoML platform integration

Leverage AtScale models with data science tools using a simple Python library and manage within your favorite notebooks.

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Programmatic Feature Creation

AcDirect integration to consistent enterprise features and third-party data sources enable programmatic feature creation and engineering for more sophisticated models.

Drive Visibility and Use of Predictions

Automatically publish predictions within dimensional models for broader visibility and self service consumption in existing BI tools.

ABOUT ATSCALE

AtScale enables smarter decision-making by accelerating the flow of data-driven insights. The company’s semantic layer platform simplifies, accelerates, and extends business intelligence and data science capabilities for enterprise customers across all industries.

Key Analytics Benefits
  • Accelerate feature engineering with consistent access to enterprise features and key business metrics.
  • Eliminate time-consuming and complex data engineering.
  • Open integration to cloud feature stores.
  • Compatible with popular open source ML libraries and platforms.
  • Integrate predictive and prescriptive analytics directly into BI tools.