Adds initial ROADMAP.md including a minimal Q2-2019 roadmap (kubeflow#3)

* Adds initial ROADMAP.md including a minimal Q2-2019 roadmap

* Provided details for each objective in the ROADMAP.

* Cleaned up after PR comments

* Fixed another typo

OWNERS

@@ -2,6 +2,6 @@ approvers:
   - ellis-bigelow
   - lluunn
 reviewers:
+  - cliveseldon
   - rakelkar
   - yuzisun
-  - cliveseldon

README.md

@@ -1,2 +1,6 @@
 # kfserving
-Model serving related infrastructure in Kubeflow
+KF Serving provides a Custom Resource Definition for serving ML Models on arbitrary frameworks. It aims at solving 80% of model serving use cases by providing performant, high abstraction interfaces for common ML frameworks like Tensorflow, XGBoost, ScikitLearn, PyTorch, and Custom containers.
+
+A KF Serving "Service" encapsulates the complexity of autoscaling, networking, health checking, server configuration, and more, to provide customers with a simple and seamless experience when deploying models.
+
+In the future, we hope to support more advanced use cases such as skew detection, explainability, and performance profiling across infrastructure configurations.

ROADMAP.md

@@ -0,0 +1,42 @@
+# KF Serving 2019 Roadmap
+## Q2 2019
+
+### Core CUJs
+Objective: "Simplify the user experience and provide a low barrier to entry by minimizing the amount of YAML necessary to deploy a trained model."
+* High Level Interfaces
+    * Deploy a Tensorflow model without specifying a Tensorflow Serving Technology.
+    * Deploy a XGBoost model without specifying a XGBoost Serving Technology.
+    * Deploy a ScikitLearn model without specifying a ScikitLearn Serving Technology.
+    * Deploy a Pytorch model without specifying a Pytorch Serving Technology.
+    * Deploy a Custom Containerized model by specifying your docker image and args.
+
+Objective: "Empower users to safely deploy production models by enabling a variety of deployment strategies." 
+* Model Rollout
+    * Rollout a model using a blue-green strategy.
+    * Rollout a model using a pinned strategy.
+    * Rollout a model using a canary strategy.
+
+Objective: "Reduce the total cost of ownership for models by minimizing the delta between provisioned resources and request load."
+* Autoscaling 
+    * Scale a model to zero.
+    * Scale a model from zero without dropping traffic.
+    * Scale a model that is GPU bound.
+    * Scale a model that is CPU bound.
+
+### High Level Work Items
+* Define the API specification (owner ellisbigelow@)
+    * Explain complete data model
+    * Document common usage patterns to meet CUJs
+
+* Implement the API specification with a CRD (owner yuzisun@)
+    * Generate a Kubebuilder CRD
+    * Define golang protos as per spec
+    * Implement ValidatingAdmissionController for API Validation
+    * Implement ReconciliationHandler to generate subresources
+
+* Integrate a KFServing component with a SeldonDeployment (owner cliveseldon@)
+    * Determine integration strategy
+    * Implement integration
+
+## Beyond Q2
+TBD