In this episode of the O’Reilly Data Show, I spoke with Ira Cohen, co-founder and chief data scientist at Anodot (full disclosure: I’m an advisor to Anodot). Since my days in quantitative finance, I’ve had a longstanding interest in time-series analysis. Back then, I used statistical (and data mining) techniques on relatively small volumes of financial time series. Today’s applications and use cases involve data volumes and speeds that require a new set of tools for data management, collection, and simple analysis.

On the analytics side, applications are also beginning to require online machine learning algorithms that are able to scale, are adaptive, and free of a rigid dependence on labeled data. I talked with Cohen about the challenges in building an advanced analytics system for intelligent applications at extremely large scale.

Semi-Supervised, Unsupervised, and Adaptive Algorithms for Large-Scale Time Series

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Semi-Supervised, Unsupervised, and Adaptive Algorithms for Large-Scale Time Series

O'Reilly Radar