Founded in 2013 by Arnaud Muller, Saagie started as a consulting company, advising companies through their innovative Artificial Intelligence-based projects.  However, it then became clear that companies needed more than Big Data strategy to help to achieve their goal, they needed infrastructure. Two years after the business was started, the first DataOps platform was launched called Saagie Plug & Play for DataOps.

Saagie is leading the way in big data analytics by providing DataOps Orchestration that accelerates and operationalizes data & analytic projects among stakehoders. Our mission is to unify people, process and technology enabling organizations to deliver projects from raw data to production in 10 weeks. Saagie delivers unmatched time-to-value, is “open by design”​ with isolated containers, strong network engineering as well as transparent security and governance bringing trust, privacy, audit and traceability to analytic projects. Saagie includes best-in-class integration with open source and commercial technologies to support today’s Big Data/AI and analytic use cases enabling global companies to realize value from their data intensive initiatives. Ultimately, Saagie provides a Plug and Play Orchestrator for DataOps that accelerates the distribution of Artificial Intelligence (AI) and Big Data to give companies a competitive advantage within their industry.

Interesting Fact…Saagie means heron in Japanese. Herons are long-legged freshwater and coastal birds that are often found in lakes.  They are a widespread, highly mobile family with a range extending across all or most of the world in appropriate habits.  The most common hunting technique is for the bird to sit motionless on the edge of or standing in shallow water and to wait until prey comes within range, thus calculating the position of the prey in the water and compensate for refraction, using the bill to spear the prey.  So why the heron?  Saagie works in a similar manner; we live in data lakes, are highly mobile, and calculate the exact position of data, then prey on even the most complex analytics to retract the best results.

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