This week on the podcast we're featuring a series of conversations from the AWS re:Invent conference in Las Vegas. I had a great time at this event getting caught up on the latest and greatest machine learning and AI products and services announced by AWS and its partners. In this episode, I'll be speaking with Nikita Shamgunov, co-founder and CEO of MemSQL, a company offering a distributed, memory-optimized data warehouse of the same name.
Nikita and I take a deep dive into some of the features of their recently released 6.0 version, which supports built-in vector operations like dot product and euclidean distance to enable machine learning use cases like real-time image recognition, visual search and predictive analytics for IoT. We also discuss how to architect enterprise machine learning solutions around the data warehouse by including components like data lakes and Spark. Finally, we touch on some of the performance advantages MemSQL has seen by implementing vector operations using Intel's latest AVX2 and AVX512