Labelbox ：数据标注自动化权威指南（英文版）（16页）.pdf

1、Thd idfnt tid utid t ia a labdltn au tma ttn 00Content01 The case for data labeling automation-102 Three types of labeling automation-2 Data unaware-2 Data semi-aware-3 Data fully aware-403 Pre-labeling:A labeling strategy proven to reduce time and effort-5 Pre-labeling for ML projects across maturi

2、ty levels-6 Real-world examples-604 Automating beyond the labeling process-8 Queueing data for labeling-8 Creating custom workflows-8 Seamless data transfers:SDKs&APIs-905 Why a data engine is essential for labeling automation-10 Automated labeling services:Risk factors to consider-1106 Labeling aut

3、omation with Labelbox-121The case for data labeling automation Developing and maintaining a performant service using supervised machine learning requires a vast amount of high-quality training data.Labeling this data is often the most time-consuming task in the machine learning process.Research has

4、shown that models perform better when they are trained on more data on a day-by-day,iteration-by-iteration basis.Training a model can take a long time,which means that iterations can then take weeks to complete,just because of the time it takes to prepare and label all the necessary training data.A

5、models performance must increase continuously as it learns new information,so faster iterations are key to a highly performant model.Machine learning projects are more likely to succeed when they iterate quickly,as this allows teams to better identify and correct for any biases in datasets and add n

6、ew datasets as use cases expand and when changes in the real world may affect previous distributions.Labeling training data also requires a lot of human effort and expertise,so this part of the process is typically also the most expensive.As the chart below illustrates,costs increase linearly with t