How does Continuous Learning Pipeline work?
Continuous Learning Pipeline works by first ingesting unstructured data. This data can be in any format, including images, videos, documents, and audio. The data is then filtered based on embeddings, file attributes, and data curation algorithms.
Once the data has been filtered, it is sent to human annotators for correction. The annotators can correct the pre-trained model inferences using a variety of methods, including consensus, domain expert review, full or partial QA, and quality scoring using honeypots.
The corrected data is then split between training, evaluation, and validation sets. New models are created automatically, trained, evaluated, and compared with the current model. The Optimal version is then deployed as the live one.
Every step and logic in the Continuous Learning Pipeline is fully configurable. This means that businesses can customize the pipeline to meet their specific needs. For example, businesses can add custom processing steps with their own code, or they can customize the code/logic of any existing node, including the model comparison algorithm.
- Learn how to build and deploy powerful computer vision pipelines
- Get a full overview of the Data Management and Annotation Studio platform
- Discover how to accelerate your data pipelines with Python SDK or REST API integrations
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Benefits of Using Dataloop Continuous Learning Pipeline
Continuous Learning Pipeline is a powerful tool that can help businesses train models more quickly and efficiently. By automating the model training process, businesses can save time and resources. Additionally, by allowing businesses to customize the pipeline, Continuous Learning Pipeline can be tailored to meet the specific needs of any business.
There are many benefits to using Dataloop Continuous Learning Pipeline, including:
- Speed: Continuous Learning Pipeline can help businesses train models more quickly than traditional methods. This is because the pipeline automates the model training process, freeing up businesses to focus on other tasks.
- Efficiency: Continuous Learning Pipeline can help businesses save resources by automating the model training process. This means that businesses can train models with less human intervention, which can save time and money.
- Flexibility: Continuous Learning Pipeline is a flexible tool that can be customized to meet the specific needs of any business.
Dataloop Continuous Learning Pipeline is a powerful tool that can help businesses train models more quickly, efficiently, and flexibly. If you are looking for a way to improve your MLOps capabilities, then Dataloop Continuous Learning Pipeline is the way to go.
