Fishing vessels are on track to generate 10 million hours of video footage annually, creating a massive ML/Ops challenge. At AI.Fish, we are building an end-to-end system enabling non-technical users to harness AI for catch monitoring and classification both on-board and in the cloud. This talk explores our journey in building these approachable systems and working toward answering an old question: How many fish are in the ocean?
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O
Speaker
Orvis Evans
3
talks
Software Engineer
AI.Fish
Orvis Evans is a Software Engineer at AI.Fish, where he co-architects ML-Ops pipelines and develops intuitive interfaces that make machine vision accessible to non-technical users. Drawing from his background in building interactive systems, he builds front-end applications and APIs that enable fisheries to process thousands of hours of footage without machine learning expertise.
Bio from: Jan 30 - AI, Machine Learning and Computer Vision Meetup
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Fishing vessels are on track to generate 10 million hours of video footage annually, creating a massive machine learning operations challenge. At AI.Fish, we are building an end-to-end system enabling non-technical users to harness AI for catch monitoring and classification both on-board and in the cloud. This talk explores our journey in building these approachable systems and working toward answering an old question: How many fish are in the ocean?
Fishing vessels are on track to generate 10 million hours of video footage annually, creating a massive machine learning operations challenge. At AI.Fish, we are building an end-to-end system enabling non-technical users to harness AI for catch monitoring and classification both on-board and in the cloud. This talk explores our journey in building these approachable systems and working toward answering an old question: How many fish are in the ocean?