Question: Is there a machine learning boilerplate that can help me focus on the creative aspects of ML projects and integrate with my existing projects?

If you need a machine learning boilerplate to let you concentrate on the creative parts of ML projects and integrate with your own work, FastML is a good choice. It's a Python-based boilerplate that automates the ML workflow with pre-built scripts for data ingestion, modeling and deployment. You can drop modules into your own projects or use it as a starting point for new ones, saving you time on integration and debugging.

Another good option is MLflow, an open-source MLOps platform that makes it easier to develop and deploy machine learning and generative AI projects. It offers a single environment for managing the entire ML project lifecycle, including experiment tracking, model management and generative AI support. MLflow supports widely used deep learning and traditional machine learning libraries, and it runs on a range of operating systems, making it a good option for improving collaboration and ML workflow efficiency.

If you're working on Large Language Models (LLMs), Humanloop is worth a look. It's a collaborative playground for developers, product managers and domain experts to build and iterate on AI features. The service includes tools to manage and optimize LLM applications, a common problem in the field, including inefficient workflows and manual evaluation. It supports widely used LLM providers and offers Python and TypeScript SDKs for integration.