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Machine learning and analytics for time series data

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Manage episode 248276627 series 61203
O'Reilly Media에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 O'Reilly Media 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.

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:

  continue reading

168 에피소드

Artwork
icon공유
 
Manage episode 248276627 series 61203
O'Reilly Media에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 O'Reilly Media 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.

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:

  continue reading

168 에피소드

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