SnapLogic Data Science set to accelerate deployment of machine learning models

By Marcus Lawrence
Californian data integration firm SnapLogic has launched its latest innovation in assisting firms with machine learning adoption: SnapLogic Data Science...

Californian data integration firm SnapLogic has launched its latest innovation in assisting firms with machine learning adoption: SnapLogic Data Science

SnapLogic recently joined Vanson Bourne for the ‘Busting Through Digital Transformation Roadblocks’ research study, surveying 500 IT decision makers from medium to large firms in the US and UK to gain insight into the issues impeding digital transformation.

The study found that 68% of those surveyed view AI and machine learning technology to be integral to digital transformation, but an industry-wide shortfall in ML expertise limiting the tech’s uptake was found by a separate McKinsey Global Institute study.

The SnapLogic Data Science self-service solution will enable companies to rapidly build and deploy machine learning programs without the need for advanced coding, circumventing the issue of limited expertise by increasing the accessibility and ease with which machine learning systems can be constructed and integrated into existing models.

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Through Data Science’s drag-and-drop-based UI, engineers, data scientists, and DevOps teams will be able to coordinate data acquisition, data analysis and preparation, model training and validation, through to full deployment.

“Every enterprise in every industry will need to employ AI and machine learning in order to keep pace with today’s most progressive businesses,” said Greg Benson, SnapLogic’s Chief Scientist, in the firm’s press release.

“With SnapLogic Data Science, we’re enabling our customers to overcome the common barriers associated with putting machine learning into practice by arming them with a full stack of self-service tools to be faster, more agile, more data-driven.”

SnapLogic’s statement goes on to explain that its new solution is the first of its kind, offering both integrable machine learning operations with strong data integration capabilities in one package.


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