AtScale, the leading provider of semantic layer solutions for modern business intelligence and data science teams, today announced at the Semantic Layer Summit an expanded set of product capabilities for organizations working to accelerate the deployment and adoption of enterprise artificial intelligence (AI). These new capabilities leverage AtScale’s unique position within the data stack with support for common cloud data warehouse and lakehouse platforms including Google BigQuery, Microsoft Azure Synapse, Amazon Redshift, Snowflake, and Databricks.
Organizations across every industry are racing to realize the true potential of their data science and enterprise AI investments. IDC predicts spending on AI/ML solutions will grow 19.6% with over $500B spent in 2023. Despite this investment, Gartner reports that only 54% of AI models built will make it into production, with organizations struggling to generate business outcomes that justify the investment to operationalize models. This disconnect creates an enormous opportunity for solutions that can simplify and accelerate the path to business impact for AI/ML initiatives.
The AtScale Enterprise semantic layer platform now incorporates two new capabilities available to all customers leveraging AtScale AI-Link:
- Semantic Predictions – Predictions generated by deployed AI/ML models can be written back to cloud data platforms through AtScale. These model-generated predictive statistics inherent semantic model intelligence, including dimensional consistency and discoverability. Predictions are immediately available for exploration by business users using common BI tools (AtScale supports connectivity to Looker, PowerBI, Tableau, and Excel) and can be incorporated into augmented analytics resources for a wider range of business users. Semantic predictions accelerate the business outcomes of AI investments by making it easier and more time to work with, share, and use AI-generated predictions.
- Managed Features – AtScale creates a hub of centrally governed metrics and dimensional hierarchies that can be used to create a set of managed features for AI/ML models. Managed features can be sourced from the existing library of models maintained by data stewards or by individual work groups. Furthermore, new
- features created by AutoML or AI platforms can also become managed features. AtScale managed features with inherent semantic context, making them more discoverable and easier to work with, consistently, at any stage in ML model development. Managed features can now be served directly from AtScale, or through a feature store like FEAST, to train models in AutoML or other AI platforms.
“Despite rising investments, greater adoption of AI/ML within the modern enterprise is still hindered by complexity,” said Gaurav Rao, Executive Vice President and General Manager of AI/ML at AtScale. “The need for AI is huge, exploration is on the rise, but many businesses are still not able to use the predictive insights AI models can generate. Here at AtScale, we can leverage our unique position in the data stack to streamline and simplify how the business can consume and use AI immediately, generating faster time to value from their enterprise AI investments.”