AtScale + Jupyter Notebooks

Learn how AtScale helps data teams simplify ML data pipelines and create a bridge between AI/ML and business intelligence teams.

AtScale + Jupyter

AtScale + Jupyter

AtScale helps data teams bridge AI to BI – simplifying ML pipelines, accelerating feature discovery, and publishing model-generated insights back to the business in BI reports and dashboards.

The Power of a Semantic Layer

An AtScale semantic layer establishes a single, governed source of metrics and dimensions that form an enterprise feature depot for data scientists. Data teams can rapidly define new views of data, build time-relative metrics, and align dimensions across data sets. Data scientists can access live data through the governed view of the AtScale semantic layer.

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Scaling Time-Series Analysis

Manage complex time-relative features via semantic models. Protect data pipelines from disruption caused by changes to underlying data or infrastructure. Bring key features discovered by models back to the semantic model for feature impact analysis.

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Publish Model Results to Broader Audiences

Model outputs — predictions, alerts, patterns — can be written back to the AtScale semantic layer. Decision-makers can work with modeled insights using the same set of BI dashboards and reports they use for historical analytics, making it easy to track predicted vs. actual metrics.

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Build a Semantic Layer on Live Cloud Data

Use the AtScale semantic layer to connect to live data on common cloud platforms. Build customized views optimized for data science use cases without losing links to live cloud data.

Resources

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

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Solution Brief

Connect AtScale Directly to Jupyter Notebook Projects

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AtScale + Jupyter Notebooks in Action

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