Search a title or topic

Over 20 million podcasts, powered by 

Player FM logo
Artwork

Content provided by The FinOps Guys - Stephen Old and Frank Contrepois, The FinOps Guys - Stephen Old, and Frank Contrepois. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by The FinOps Guys - Stephen Old and Frank Contrepois, The FinOps Guys - Stephen Old, and Frank Contrepois or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.
Player FM - Podcast App
Go offline with the Player FM app!

WNiCF - Interview with Henk - Time series, forecasts and anomaly detections, all hard problems to crack.

38:12
 
Share
 

Manage episode 470863788 series 3553457
Content provided by The FinOps Guys - Stephen Old and Frank Contrepois, The FinOps Guys - Stephen Old, and Frank Contrepois. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by The FinOps Guys - Stephen Old and Frank Contrepois, The FinOps Guys - Stephen Old, and Frank Contrepois or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

Send us a text

  • We discussed the challenges of working with time series data, particularly in the context of machine learning and AI, highlighting the complexity and the need for automation in feature engineering.
  • The importance of balancing accuracy and complexity in model creation was emphasized, with a focus on avoiding overfitting and ensuring models remain effective in real-world applications.
  • The potential integration of business context data, such as sales data, with cloud consumption data to enhance anomaly detection and forecasting models was proposed.
  • The discussion touched on the economic value of anomaly detection, with a focus on proving that early detection can lead to significant cost savings.
  • The target audience for the anomaly detection system was identified as FinOps managers, who would use the system to manage cloud-related financial topics and coordinate with engineers to address anomalies.

  continue reading

85 episodes

Artwork
iconShare
 
Manage episode 470863788 series 3553457
Content provided by The FinOps Guys - Stephen Old and Frank Contrepois, The FinOps Guys - Stephen Old, and Frank Contrepois. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by The FinOps Guys - Stephen Old and Frank Contrepois, The FinOps Guys - Stephen Old, and Frank Contrepois or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

Send us a text

  • We discussed the challenges of working with time series data, particularly in the context of machine learning and AI, highlighting the complexity and the need for automation in feature engineering.
  • The importance of balancing accuracy and complexity in model creation was emphasized, with a focus on avoiding overfitting and ensuring models remain effective in real-world applications.
  • The potential integration of business context data, such as sales data, with cloud consumption data to enhance anomaly detection and forecasting models was proposed.
  • The discussion touched on the economic value of anomaly detection, with a focus on proving that early detection can lead to significant cost savings.
  • The target audience for the anomaly detection system was identified as FinOps managers, who would use the system to manage cloud-related financial topics and coordinate with engineers to address anomalies.

  continue reading

85 episodes

All episodes

×
 
Loading …

Welcome to Player FM!

Player FM is scanning the web for high-quality podcasts for you to enjoy right now. It's the best podcast app and works on Android, iPhone, and the web. Signup to sync subscriptions across devices.

 

Copyright 2025 | Privacy Policy | Terms of Service | | Copyright
Listen to this show while you explore
Play