The major Cloud Service Provider (CSP) hyperscalers have established themselves pretty much from the outset as essentially Software-as-a-Service (SaaS) entities that s.
Monte Zweben proposes a whole new approach to MLOps that allows to scale models without increasing latency by merging a database, a feature store, and machine learning.
New Spark 3.0 connector will appeal to users too, analyst says
Lindsay Clark Wed 5 May 2021 // 18:10 UTC Share
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Distributed NoSQL database Aerospike is introducing set indexes and SQL operations within expressions in the pursuit of greater machine learning efficiency via its Apache Spark 3.0 connector.
Speaking to
The Register, chief product officer Srini Srinivasan claimed the combined tweaks could help reduce the feedback cycle to improve ML models from days to hours.
A key-value and multi-modal database, Aerospike can run on the edge to support so-called real-time decisions based on pre-existing ML models in applications such as fraud detection. It is also used to feed data back into the ML model management commonly used by data pipeline platform Apache Spark to ensure models reflect changes to data patterns in the real world.