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 →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 →Spark gets automation: Analyzing code and tuning clusters in production
Spark is the hottest big data tool around, and most Hadoop users are moving towards using it in production. Problem is, programming and tuning Spark is hard. But Pepperdata and Alpine Data bring solutions to lighten the load.
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?
Read More →DataOps: Changing the world one organization at a time
Uncertain about the benefits of going data-driven? Not sure how to get there? Here's some advice from the people who built Facebook's data infrastructure
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