Question: Is there a framework that provides high-level convenience features to accelerate experimentation cycles for researchers and scientists?

If you're looking for a framework that offers higher-level convenience features to speed up experimentation cycles, Keras could be a great option. Keras is a deep learning API designed to be fast and easy to use, a good fit for researchers and scientists. It supports several backend frameworks, has abundant documentation and tutorials, and more than 150 code examples to get you started. For computer vision, natural language processing, generative AI and other tasks, Keras' flexibility and scalability means you can run big experiments without worrying about performance.

Another tool worth a look is Layer, which uses AI to automate some aspects of research. It offers features like Instant Extraction, Precise Answers and PDF Insights to help you find the information you need and analyze it. That can be helpful for researchers who have to dig through heavy documents and dense reports, freeing up their time for higher-level thinking.

If you're interested in feature management and experimentation, Statsig offers a full-featured platform. It's got tools to manage feature flags, run experiments and analyze user behavior. Statsig's platform lets teams speed up experimentation velocity and make data-driven decisions, and it's useful for small teams and large enterprises.

Last, Notably is an all-purpose research platform that marries traditional research methods with AI technology. It's got AI-powered templates for data analysis, instant analysis tools and a multi-view workspace to help you synthesize insights from big data. It's good for product researchers, market researchers and academic researchers who want to improve the quality and speed of their research.