![Anup J.](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "Anup J.") AJ Anup J. Machine Learning Engineer Small-Business (50 or fewer emp.) 5/30/2023 "The only Model Serving Tool You Need" 5/5 What do you like best about BentoML? 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. Review collected by and hosted on G2.com. What do you dislike about BentoML? Writing configs for Bento can get unnecessarily involved and complex. It feels like a part of the process that can be automated in the library rather than manually filling it out. Deploying a custom model in Bento is fairly difficult. Its not impossible, but its hardly a breeze either involving build custom loaders and then all of their preprocessing functions. Deploying Yatai for a production build is again a unpleasant task Review collected by and hosted on G2.com. What problems is BentoML solving and how is that benefiting you? BentoML helps us to solve and streamline our model deployment and serving operations. Its Yatai interface helps us to create performant Kubernetes deployments that we can deliver to customers confidently. It also helps to reduce the overhead on our ML Engineers and DevOps department by have a smooth approach that the builders of the models can use to deploy their own rather than be dependent on an other team Review collected by and hosted on G2.com. Show More Validated ReviewerSource: Organic See what 2 reviewers think of BentoML 5.0 out of 5 ยท Verified reviews from real users [ Read all reviews ](https://www.g2.com/products/bentoml/reviews)