We see that enterprises looking to implement data science and AI in business applications struggle with complex, siloed data pipelines that require very long deployment periods at extremely high costs.
“In reality, data scientists spend very little of their time on actual datascience. Instead, they dedicate too much time to solving the ‘before’ issues of ingestion, plumbing, waiting for data and waiting for computing, and the ‘after’ issues of deployment and productization along with their data engineering peers.”
“With Iguazio, we are then able to provide a distributed application and database layer that can treat data very fast, with non-blocking invocations, so that we can process many events in parallel in the same process, overcoming the lack of concurrency downfall of most serverless AI pipeline approaches,” said Nissan-Messing.
“Gartner released an amazing number in  that 85 percent of such projects are failing. The main reasons for the failures are complexity and the huge number of technologies, and the way data has to be moved from one platform to another,” said Somekh.