Data Challenges for Computer Vision Teams（00:30:07 - 00:35:05）
DataOps for Computer Vision

An overview of DataOps for computer vision and data-related challenges that any computer vision teams have to deal with.

Implementing state-of-the-art architectures, tuning model hyper-parameters, and optimizing loss functions are the fun parts of computer vision. As good as it may seem, behind each model that gets deployed into production are data labelers and data engineers responsible for building a high-quality training dataset that serves as the model’s input. In this talk, I will provide an overview of DataOps for computer vision, outline the key data-related challenges that any computer vision teams have to deal with, and propose specific functions of an ideal DataOps platform to address these challenges.

Table of Contents:
00:00 Introduction
02:14 What is DataOps
07:33 Why DataOps for Computer Vision
11:42 DataOps Key Principles
23:56 DataOps Pipeline for the Computer Vision Stack
30:07 Data Challenges for Computer Vision Teams
35:05 The Future of Modern Computer Vision Stack
40:47 QnA

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#dataops #computervision #ai

#computer vision #data ops #data labelers #data engineers #tuning hyperparameters #mlops #chatbot #computer vision stack #what is dataops