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 →Will the real Elon Musk please stand up? Autonomous bots and synthesized speech in the public domain
The ability to create virtual clones that appear to think and talk like the real thing is very much real, as it has been done for Elon Musk and Barack Obama. We discuss techniques and potential with the people behind them.
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 →NBA analytics: Going data pro
For the NBA, like every other sports league, awards are important. They can generate attention, spur debate, make money, and involve fans, players, and experts, among others. Is there data science and analytics behind them — can there or should there be? We picked the NBA Most Improved Player award as an example to analyze some aspects of data-driven culture.
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 →Artificial intelligence on Hadoop: Does it make sense?
MapR just announced QSS, a new offering that enables the training of complex deep learning algorithms. We take a look at what QSS can offer, and examine AI on the Hadoop landscape.
Read More →Graph databases and RDF: It’s a family affair
RDF is a graph data model you've probably either never heard of, or already dismissed. Why is that, could there be value in it, and how does it differ from the most popular graph data model out there?
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.
Read More →Advertising data going forward: Transparency and scale
The problem with advertising data and what to do about it. Plus, the future of big data architecture, and other stories from the Ad Tech trenches
Read More →Caffe2: Deep learning with flexibility and scalability
As the AI landscape continues to evolve, a new version of the popular Caffe open source deep learning framework has been released. Caffe2 is backed by Facebook and features a wide array of partnerships to make it as flexible and scalable as possible. But is that enough to make Caffe2 a winner?
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