Labellerr is flexible in terms of pricing and no. of persons using it.
Small teams and individuals can also start with Labellerr to test capabilities or validate early-stage experiments and an enterprise can use it for their large datasets. The pricing will be flexible based on the usage of the tool.
With Labellerr, there is an option to choose different pre-trained models to speed up the annotation project. It helps in identifying objects, drawing the annotations and also auto-label.
It is faster then AWS SageMaker in terms of data collection, data preparation, annotation, model training and deployment as it provide full support in project management.
Smart Feedback Loop
Labellerr has developed "Smart feedback loop" that could be customized for a different use cases with very less effort and then it would cover the iterative nature of model training fully autonomous.
Custom ML model building has been a huge challenge for the ML community, as it requires a good amount of capital and time, to begin with. Customization is a smarter way is to bring automation to your continuous training data pipeline.