Machine learning and analytics for time series data

In this episode of the Data Show, I speak with Arun Kejariwal of Facebook and Ira Cohen of Anodot (full disclosure: I’m an advisor to Anodot). This conversation stemmed from a recent online panel discussion we did, where we discussed time series data, and, specifically, anomaly detection and forecasting. Both Kejariwal (at Machine Zone, Twitter, and Facebook) and Cohen (at HP and Anodot) have extensive experience building analytic and machine learning solutions at large scale, and both have worked extensively with time-series data. The growing interest in AI and machine learning has not been confined to computer vision, speech technologies, or text. In the enterprise, there is strong interest in using similar automation tools for temporal data and time series.

We had a great conversation spanning many topics, including:

• Why businesses should care about anomaly detection and forecasting; specifically, we delve into examples outside of IT Operations & Monitoring.
• (Specialized) techniques and tools for automating some of the relevant tasks, including signal processing, statistical methods, and machine learning.
• What are some of the key features of an anomaly detection or forecasting system.
• What lies ahead for large-scale systems for time series analysis.

Related resources:

• “Product management in the machine learning era” – a new tutorial at the Artificial Intelligence Conference in London
• “One simple chart: Who is interested in Apache Pulsar?”
• Ira Cohen: “Semi-supervised, unsupervised, and adaptive algorithms for large-scale time series”
• “Got speech? These guidelines will help you get started building voice applications”
• “RISELab’s AutoPandas hints at automation tech that will change the nature of software development”
• Ameet Talwalker: “How to train and deploy deep learning at scale”