Human-in-the-loop (HITL) machine learning is a collaborative approach that integrates human input and expertise into the lifecycle of machine learning (ML) and artificial intelligence systems. Humans actively participate in the training, evaluation, or operation of ML models, providing valuable guidance, feedback, and annotations. Through this collaboration, HITL aims to enhance the accuracy, reliability, and adaptability of ML systems, harnessing the unique capabilities of both humans and machines.
Humans can interact with HITL systems in various ways, including:
While ML models possess remarkable capabilities, they can benefit from human expertise in areas requiring judgment, contextual understanding, and handling incomplete information. HITL bridges this gap by incorporating human input and feedback into the ML pipeline.
This human collaboration enhances adaptability and allows models to evolve with changing user preferences and real-world scenarios. By integrating the human element, we empower ML systems to navigate the complexities and nuances that often challenge purely algorithmic approaches.
There are a number of benefits to using HITL, including:
What are the use cases of human-in-the-loop?
HITL can be used in a variety of applications, including:
HITL can be used to label images for training ML models that can classify images. This can be used for a variety of applications, such as:
HITL may be used to label text for training ML models that can understand natural language. This can be used for a variety of applications, such as:
It can be used to label audio data for training ML models that can recognize speech. This can be used for a variety of applications, such as:
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