SnapLogic, provider of an integration platform, has announced SnapLogic Data Science, a new self-service solution to accelerate the development and deployment of machine learning with minimal coding.
SnapLogic Data Science allows data engineers, data scientists, and IT/DevOps teams to manage and control the entire machine learning lifecycle – including data acquisition, data exploration and preparation, model training and validation, and model deployment – all from within the SnapLogic integration platform.
The solution helps break down traditional barriers that can undermine machine learning initiatives by providing a common platform for machine learning visibility and collaboration across teams.
According to SnapLogic research, 68% of IT decision makers consider artificial intelligence and machine learning to be vital to accelerating their transformation projects. At the same time, McKinsey Global Institute predicts that the U.S. alone will be short 250,000 data scientists by 2024.
Machine learning initiatives are hampered by limited access to data science talent as well as a lack of automated data access to fuel model building. By bridging the data science skills gap and automating the machine learning lifecycle, SnapLogic Data Science makes end-to-end machine learning accessible to enterprises of all sizes for the first time.
According to Greg Benson, chief scientist at SnapLogic, every enterprise in every industry will need to employ AI and machine learning in order to keep pace with today’s most progressive businesses.
However, he noted, most companies fall flat in actualizing machine learning because they don’t have the talent or financial resources to make the most of their data.
With the new solution, he said, SnapLogic is helping 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.
More information is available about SnapLogic Data Science on the SnapLogic website.