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MLOps 101: Scoping, Latency, Data Curation, and Continuous Model Retraining - ML 143

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Content provided by Charles M Wood. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Charles M Wood 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://ro.player.fm/legal.
Ben and Michael dive into the world of machine learning operations (MLOps) and discuss the complexities of building a computer vision pipeline to detect fishing boats at ports. They unpack the intricacies of MLOps basics and the challenges of implementing an effective computer vision model for traffic optimization and data collection at ports. From discussing the importance of exploratory data analysis (EDA) and data cleaning for image classification to the intricacies of continuous integration and deployment, this episode provides invaluable insights into the practical application of machine learning in real-world scenarios.Sponsors
Advertising Inquiries: https://redcircle.com/brands
Privacy & Opt-Out: https://redcircle.com/privacy
Become a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.
  continue reading

209 episoade

Artwork
iconDistribuie
 
Manage episode 406412499 series 2977446
Content provided by Charles M Wood. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Charles M Wood 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://ro.player.fm/legal.
Ben and Michael dive into the world of machine learning operations (MLOps) and discuss the complexities of building a computer vision pipeline to detect fishing boats at ports. They unpack the intricacies of MLOps basics and the challenges of implementing an effective computer vision model for traffic optimization and data collection at ports. From discussing the importance of exploratory data analysis (EDA) and data cleaning for image classification to the intricacies of continuous integration and deployment, this episode provides invaluable insights into the practical application of machine learning in real-world scenarios.Sponsors
Advertising Inquiries: https://redcircle.com/brands
Privacy & Opt-Out: https://redcircle.com/privacy
Become a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.
  continue reading

209 episoade

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