Guides# This chapter introduces the key features of BentoML. We recommend you read Quickstart before diving into this chapter. Services Understand the BentoML Service and its key components. Input and output types Customize the input and output type of BentoML Services. Deployment Gain a general understanding of BentoCloud deployment. Containerization Create an OCI-compliant image for your BentoML project and deploy it anywhere. Build options Customize the build configurations of a Bento. Model Store Use the BentoML local Model Store to manage your models in a unified way. Distributed Services Create distributed Services for advanced use cases. Concurrency Set concurrency to enable your Service to handle multiple requests simultaneously. Testing Create tests to verify the functionality of your model and the operational aspect of your Service. Clients Use BentoML clients to interact with your Service. Adaptive batching Enable adaptive batching to batch requests for reduced latency and optimized resource use. ASGI integration Integrate ASGI frameworks in a BentoML Service to provide additional features to exposed endpoints. Configurations Customize the runtime behaviors of your Service. Lifecycle hooks Confgiure hooks to run custom logic at different stages of a Service’s lifecycle.