Bentoml

* Improved BentoML module import time by around 3-4x
* List deployments command now shows "age" column denoting how long the deployment has been created
* Fixed a bug where serverless deployment failed to install required plugins

Docs:
* Updated documentation site https://bentoml.readthedocs.io/

New Features:
* Support for both Keras and tensorflow.keras module in KerasModelArtifact
* New serialization option for KerasModelArtifact that stores model in json and weights files (by ghunkins )
* `bentoml list deployments` provides clean table outputs now
* Support for AWS S3 based BentoML repository (Beta)

Bug fixes:
* Fixed error with old click version 335
* Fixed REST API Server issue on Windows platform 333

* Enhancement to Serverless deployment and SageMaker deployment
* Updated default version string format for user-defined BentoService
* Added the `versioneer` interface on BentoService for users to define a customized versioning format
* Added '--force' option to `bentoml deployment delete` command
* Updated clipper base image to 0.4.1

For BentoML developers:
* BentoML now packages local BentoML dev branch when bundling a BentoService for deployment

* Introduced SklearnModelArtifact, adding more scikit-learn specific optimizations over previous general PickleArtifact
* Fixed a number of issues with AWS Lambda Serverless deployment
* Improved error message and CLI outputs of AWS SageMaker deployment

* Fixed an issue with initializing BentoML logging and repository file direcotry

* Redesigned deployment component available now, take a look at the deploy command:`bentoml deployment --help`

* Multiple image support in ImageHandler

* Yatai Service Beta Release - a new component in BentoML providing a model registry and deployment manager for your BentoService. It's a stateful service that can run in your local machine for a personal project, or hosted on a server and shared by a machine learning team.

bentoml-release-v0.3.4
* Add `pip_dependencies` option to `bentoml.env` decorator, and making it the recommended approach for adding PyPI dependencies
* Fixed an issue related OpenAPI doc spec with ImageHandler

BentoML Developer Notes
* DEV: added versioneer.py for version management, now using git tags to manage releases
* DEV: Yatai service protobufs and generated interfaces are in the REPO now