Data quality can make or break a machine learning project. Work with our data experts to develop an effective process—whether that's active learning, weak supervision, or just the confidence you're collecting and annotating the right data. We can help with error analysis, continuous evaluation systems, performance metric choices, dataset auditing, and other important evaluation decisions.
Have spaCy core developers review your existing code to help ensure your model is as accurate, efficient and maintainable as you need it to be. We'll review your existing code and provide practical suggestions to make future modification easier, get your code production-ready and better take advantage of all of spaCy's functionality.
Most NLP pipelines aren't just a single model, but a collection of modular components that perform different tasks. We can help by developing and configuring custom components for your pipeline—incorporating best practices and helpful pipeline design patterns that work for the task at hand.
Data annotation campaigns can be complex and require significant expertise to avoid common pitfalls. Our annotation coaches can help you meet your data quality goals and avoid annotation biases. We believe useful data results from consistent annotation workflows, guidelines and best practices that deliver a great user experience for annotators.
We're ready to help and excited to learn about your project ✨
