In fact, the manufacturers have often made fundamental design errors. In some cases, the smart devices only work if they are connected to the Internet and can reach the manufacturers' servers. That becomes a significant point of failure when the manufacturer ends product support, as happened with the Sony Dash and the early Nest thermometer. Including a remote Internet-connected server into a control loop also introduces a significant and variable lag into the control loop which can introduce instability.
Even worse, in their rush to connect their "things" to the Internet, manufacturers have exposed vulnerabilities that have been exploited by hackers. Automobiles have been taken over remotely, home routers have been enlisted into a botnet for carrying out DDoS attacks, the public power grid has been brought down in some areas...
What will it take to make IoT devices secure? Why aren't the manufacturers paying attention?
Until security is addressed, the data analytics promise of IoT will be more risk than reward.
Heating up: TensorFlow
Who: Data scientists
TensorFlow is Google's open source machine learning and neural network library, and it underpins most if not all of Google's applied machine learning services. The Translate, Maps, and Google apps all use TensorFlow-based neural networks running on our smartphones. TensorFlow models are behind the applied machine learning APIs for Google Cloud Natural Language, Speech, Translate, and Vision.
Data scientists can use TensorFlow, once they can get over the considerable barriers to learning the framework. TensorFlow boasts deep flexibility, true portability, the ability to connect research and production, auto-differentiation of variables, and the ability to maximize performance by prioritizing GPUs over CPUs. Point your data scientists toward my tutorial or have them look into the simplified Tensor2Tensor library to get started.
Heating up: MXNet
Who: Data scientists
MXNet (pronounced "mix-net") is a deep learning framework similar to TensorFlow. It lacks the visual debugging available for TensorFlow but offers an imperative language for tensor calculations that TensorFlow lacks. The MXNet platform automatically parallelizes symbolic and imperative operations on the fly, and a graph optimization layer on top of its scheduler makes symbolic execution fast and memory efficient.
Cooling down: Batch analysis
Who: BI/BA pros, data scientists
Running batch jobs overnight to analyze data is what we did in the 1970s, when the data lived on 9-track tapes and "the mainframe" switched to batch mode for third shift. In 2017, there is no good reason to settle for day-old data.
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