From big data to AI: Where are we now, and what is the road forward?
It took AI just a couple of years to go from undercurrent to mainstream. But despite rapid progress on many fronts, AI still is something few understand and fewer yet can master. Here are some pointers on how to make it work for you, regardless of where you are in your AI journey.
Read More →Radio data and the future of broadcasters: Using attribution analysis to measure consumer behavior
Is it possible to determine how effective radio advertising really is? TagStation says yes, using a method called attribution analysis, and this may be key for the future of broadcasters.
Read More →Moving fast without breaking data: Governance for managing risk in machine learning and beyond
How do you resolve the tension between the need to build and deploy accurate machine learning models fast, and the need to understand how those models work, what data they touch upon, and what are the implications? Immuta says data governance is the answer.
Read More →Planet analytics 1.0: From the UN lab to the globe
The United Nations (UN) is developing initiatives that leverage data and analytics to measure and streamline sustainable development goals. While this is work in progress, there are some fundamental questions as to its effectiveness.
Read More →The future of the future: Spark, big data insights, streaming and deep learning in the cloud
Apache Spark is hailed as being Hadoop's successor, claiming its throne as the hottest Big Data platform. What the founding fathers of Spark are saying and doing about its future and its positioning in the market has never been more timely.
Read More →Raiders of the storm: The data science behind weather prediction
What kind of data and techniques are used to model and predict weather and climate? How do you reduce uncertainty and communicate complexity? Are Harvey and Irma signs of climate change, and is it going to get worse?
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 →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 →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.
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