Forefront Connector for AI agents.

It lets you start and manage fine-tuning jobs directly from your AI client. You can kick off training runs and monitor their progress without switching to a separate web dashboard.

Yes, you can create, list, and track data pipelines. This makes it much easier to organize large amounts of model outputs for later use.

It does. You can use it to start fine-tuning jobs on base models using your own specific training and validation datasets.

You can create a pipeline to automatically collect and organize LLM outputs. This saves you from having to manually copy and paste results into a spreadsheet.

Yes, you can retrieve specific data samples from any of your active pipelines. This is great for auditing your model's performance during development.

It's ideal for that. By using pipelines, you can gather thousands of samples at scale and then convert those selections into structured datasets for training.

You can use the create_chat_completion capability. Provide the model name and an array of messages in chat-ml format to receive the generated response.

Yes! Use the create_fine_tune capability by specifying the name of your fine-tuned model, the baseModel, and the trainingDataset ID to begin training.

First, create a pipeline using create_pipeline. Once created, you can use add_pipeline_data to log messages, user IDs, and custom metadata directly into that pipeline.