The Machine Teaching Solution for Data-Centric AI
Accelerate your AI development by 10-100x with the world's fastest data-centric workflow. Watchful makes it easy to develop and fine-tune AI systems by focusing on the most important part: The Data.
Data-centric companies use Watchful to accelerate their AI development by 10-100x, a speed and scale that would be impossible with legacy solutions.
“Watchful makes it easy to have full control and confidence over labeling large datasets in a new, systematic way.”
Develop and Fine-Tune Your AI
Use Watchful to holistically explore, classify, annotate and validate the unique datasets that power today’s AI initiatives and business processes.
Prompt Engineering and Fine Tuning Foundation Models
• Create robust LLM-enabled systems without the headaches of building one large and brittle prompt by "braiding" together multiple prompts within Watchful
• Combine the outputs of multiple prompts and other sources of information (e.g., dictionaries, keywords, other models)
• Let Watchful guide you to the optimal point of deciding between prompting and fine-tuning
Not using LLMs? No problem.
• Watchful makes it easy for anyone to annotate and enrich datasets
• Rapidly slice-and-dice your data with real-time results and feedback
• AI-assisted development helps you automatically improve the quality of your data, guiding you to identify and correct errors
Data-Centric from the Ground Up
Place your “Experts-in-the-Loop” from the start of your dataset development.
Search by characteristics of the data, either inherent or latent/semantic.
Let Watchful recommend your next actions to automatically improve your labels by introducing new weak signals, or focusing your attention on particularly important parts of the data.
Control Your Data, Your Way
Self-hosted docker images makes it easy to run Watchful wherever you need to.
Integrate with your existing MLOps system or workflows.
Automate and scale pipelines using Watchful’s SDK.
An Interactive and Fully Guided Workflow
Let Watchful guide you through your dataset with ML-assisted feedback and error analysis.
Get feedback in real-time to drive insights and capture impactful features and domain knowledge.
Quickly iterate to improve your data quality through interactive visualizations and real-time feedback.
Hand Labeling Considered Harmful
Labeling training data is the one step in the data pipeline that has resisted automation, and evolution. It’s time to change that.
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