Question: I need a solution that enables me to visualize and curate large datasets for training and evaluation, can you recommend one?

If you're looking for a way to visualize and curate data for training and evaluating AI models, Dataloop is an all-in-one platform that handles data curation, model management, pipeline orchestration and human-in-the-loop feedback to speed up AI application development. It can handle unstructured data like images, videos and text, and has tools like automated preprocessing and embeddings to find similarity, which can be useful for collaboration and development efficiency.

Another good option is Encord, a full-stack data development platform for building predictive and generative computer vision applications. Encord has tools for ingesting, cleaning, curating, auto-labeling and evaluating model performance. With its Annotate tool, you can apply auto-labels and create custom workflows, and Active provides monitoring, debugging and model performance evaluation. That makes Encord a good option for speeding up AI development cycles while ensuring high-quality training data.

For teams building GenAI applications, HoneyHive is a mission-critical AI evaluation, testing and observability platform. It provides a single LLMOps environment for collaboration, testing and evaluation, along with automated CI testing, production pipeline monitoring and dataset curation. HoneyHive supports use cases like debugging, online evaluation and user feedback, so it's a good option for managing and optimizing AI models.

Last, Airtrain AI is a no-code compute platform geared for data teams that have to wrangle big data pipelines. It includes a Dataset Explorer for visualizing and curating data, an LLM Playground for fine-tuning models, and AI Scoring for evaluating models. With its community support system and three pricing tiers, Airtrain AI is designed to make LLMs more accessible and affordable, so you can quickly evaluate and deploy custom AI models.