Alice, the making of: Behind the scenes with the new AI assistant from Yandex
Did you ever wonder what it's like to build an AI personal assistant, or to bridge the language gap? Hint: There's big data and machine learning involved.
Read More →TigerGraph, a graph database born to roar
Imagine your database of choice blown out of the water by a startup emerging from stealth. TigerGraph may have done just that for graph databases.
Read More →Insight platforms as a service: What they are and why they matter
The convergence of cloud, automation and collaboration has created a new class of offerings for data driven insights. We take a look at their defining characteristics, featuring analysis from Forrester and commentary from Qubole.
Read More →Semantic data lake architecture in healthcare and beyond
Data lakes can be a great asset, but they need an array of elements to work properly. We take a look at how it works for Montefiore Health System and discuss the role of semantics and graph databases in the data lake architecture.
Read More →Supercharging your image: Machine learning for photography applications
Advanced capabilities for image retrieval and processing are relatively new and powered to a large extent by advances in machine learning technology. We present a brief history of this space, and share the story of how Shutterstock has embraced this technology and what it does for them.
Read More →How machine learning is taking on online retail fraud
Fraud is one of the biggest causes of lost revenue for online retailers. Fraugster and Riskified, two startups that operate in this space, share their insights and methods for safeguarding online retail.
Read More →Alibaba: Building a retail ecosystem on data science, machine learning, and cloud
What does it take to compete in a global arena in which retail and cloud are increasingly intertwined? Domain-specific data science and machine learning for the masses, according to Alibaba.
Read More →Kafka: The story so far
Hard problems at scale, the future of application development, and building an open source business. If any of that is of interest, or if you want to know about Kafka, real-time data, and streaming APIs in the cloud and beyond, Jay Kreps has some thoughts to share.
Read More →Automating automation: a framework for developing and marketing deep learning models
Are you sold on the benefits of adding automation to your stack, but put off by the high entry barrier to this game? The NeoPulse Framework promises to ease the burden of developing Deep Learning models by introducing a number of interesting concepts.
Read More →The continuing rise of graph databases
Graph technology is well on its way from a fringe domain to going mainstream. We take a look at the state of the union in graph, featuring Neo4j's latest release and insights as well as data and opinions from Cloudera, DataStax, and IBM.
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