I really like how bentoml's framework is built for handling incoming traffic's, i really like its feature of workers as an ai developer running nlpmodels on scalable is crucial bentoml helps me to easily of building a service which can accept multiple request using the help of workers, i also like its feature of bento building and dockerization, in traditional method to dockerize we create a flask or django or gradio... service and then write a dockerfile initialize a nvidia support in docker, this all is the work of devops engineer but bentoml come to rescue here just write a bentofile.yaml where you specify you service cuda version libraries to install, system packages to install and just bentoml build and then bentoml containerize boom bentoml just containerized for you it did write a dockerfile for you and saved the time for write dockerfile and building it, i really like this about bentoml, it has good customer support as well it has a slack environment where the developers of bentoml are deeply engaged with the solving of bentoml users issues which they are facing

One word simplicity. ML model serving is a complex beast, and Bento is the only tool that makes it a remotely simple experience. The ability to spin up a fairly performant Docker-based microservice for your model in about 15 lines of code has saved me in many tight spots. Bento's model saving and versioning abilities are also beneficial in tracking down issues with both model deployment and model efficacy in the wild. It helps to quickly and automatically rollback versions of a model. Combined with Yatai Bento's dashboard for monitoring and Kubernetes deployment framework , these capabilities make many MLOps tasks painless. Finally, a word about the extensive integrations that BentoML has to the broader Python Data Science ecosystem. This allows Bento to be incrementally and non-intrusively attached to a data science toolkit.