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Connect AtScale Directly to Jupyter Notebook Projects

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.
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The Data Dilemma for Data Scientists

Data Science is rapidly changing the modern enterprise, redefining what it takes to be competitive across most every industry. The competition for data scientists is fierce with every organization competing for scarce skills. One of the challenges to getting more from existing resources is the inefficiencies of working with data. Some estimates show data scientists spending 60% or more of their time wrangling data.

The Solution

Data Scientists can access live cloud data through an AtScale semantic layer using simple python scripts. This approach simplifies data pipelines and can accelerate feature engineering. 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 from 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 connects Jupyter to the cloud

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

The AtScale AI-Link 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

Extend role-based security and governance policies of source data to analytics consumptionLeverage AtScale models with data science tools using a simple Python library and manage within your favorite notebooks..

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Programmatic Feature Creation

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

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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.