Dataloop

Collect LiDAR and camera data from test vehicles.

Use Dataloop's annotation tools to label objects of interest (e.g., pedestrians, vehicles, traffic signs).

Train a deep learning model using the annotated data.

Evaluate the model's performance and iterate by collecting more data and refining annotations.

Upload medical images (e.g., X-rays, CT scans) to Dataloop.

Use Dataloop's annotation tools to identify and label anatomical structures and abnormalities.

Train a machine learning model to detect diseases or conditions automatically.

Deploy the model to assist radiologists in diagnosis and treatment planning.

Capture images of products on store shelves.

Use Dataloop's annotation tools to label each product.

Train a computer vision model to recognize products automatically.

Integrate the model into a retail application to track inventory and provide product recommendations.

Generate outputs from a GenAI model.

Present these outputs to human reviewers for feedback on quality, safety, and alignment with desired behavior.

Use the human feedback to train a reward model that scores the outputs.

Use the reward model to fine-tune the GenAI model using reinforcement learning algorithms.

Ingest and index a knowledge base (e.g., documents, articles, databases) into Dataloop.

When a user query is received, retrieve relevant information from the knowledge base.

Use a GenAI model to generate a response based on the query and the retrieved information.

Evaluate the quality of the generated response and refine the RAG workflow as needed.