feat: Add 2020 roadmap

ROADMAP.md

@@ -1,43 +1,31 @@
 # Katib 2020 Roadmap
 
-This document provides a high level view of where Katib will grow in 2020. 
+## New Features
 
-The original Katib design document can be found [here](https://docs.google.com/document/d/1ZEKhou4z1utFTOgjzhSsnvysJFNEJmygllgDCBnYvm8/edit#heading=h.7fzqir88ovr).
+### Hyperparameter Tuning
 
-# Katib 1.0 Readiness
+- Support Early Stopping [#692](https://github.com/kubeflow/katib/issues/692)
 
-* Stabilize APIs for Experiments
-	* Reconsider the design of Trial Template [#906](https://github.com/kubeflow/katib/issues/906)
-	* Early Stopping [#692](https://github.com/kubeflow/katib/issues/692)
-	* Resuming Experiment [#1061](https://github.com/kubeflow/katib/issues/1061), [#1062](https://github.com/kubeflow/katib/issues/1062)
-* Fully integrate Katib with existing E2E examples:
-	* Xgboost
-	* Mnist
-	* GitHub issue summarization
-* Publish API documentation, best practices, tutorials
-* [Issues list](https://github.com/kubeflow/katib/issues)
+### Neural Architecture Search
 
-# Enhance HP Tuning Experience
+- Support Advanced NAS Algorithms like DARTs, ProxylessNAS [#461](https://github.com/kubeflow/katib/issues/461)
 
-The objectives here are organized around the three stages defined in the CUJ:
+### Other Features
 
-## 1. Defining Model and Parameters
+- Support Auto Model Compression [#460](https://github.com/kubeflow/katib/issues/460)
+- Support Auto Feature Engineering [#475](https://github.com/kubeflow/katib/issues/475)
 
-Integration with KF distributed training components
-* TFJob
-* PyTorch
-* Allow Katib to support other operator types generically [#341](https://github.com/kubeflow/katib/issues/341)
+## Enhancements
 
-## 2. Configuring a Experiment
-* Supporting additional suggestion algorithms [#15](https://github.com/kubeflow/katib/issues/15)
+### Hyperparameter Tuning
 
-## 3. Tracking Model Performance
-* UI enhancements: allowing data scientists to visualize results easier
-* Support for persistent model and metadata storage
-	* Ideally users should be able to export and reuse trained models from a common storage
+- Delete Suggestion deployment after Experiment is finished [#1061](https://github.com/kubeflow/katib/issues/1061)
+- Save Suggestion state after deployment is deleted [#1062](https://github.com/kubeflow/katib/issues/1062)
+- Reconsider the design of Trial Template [#906](https://github.com/kubeflow/katib/issues/906)
+- Add validation for algortihms (a.k.a suggestions) [#1126](https://github.com/kubeflow/katib/issues/1126)
 
-# Test and Release Infrastructure
+### Neural Architecture Search
 
-* Improve e2e test coverage
-* Improve test harness
-* Enhance release process; adding automation (see https://bit.ly/2F7o4gM) 
+- Refactor structure for NAS algorithms [#1125](https://github.com/kubeflow/katib/issues/1125)
+- Refactor the design for NAS model constructor[#1127](https://github.com/kubeflow/katib/issues/1127)
+- Katib UI fixes and enhancements