Add docs for consuming features in online environment

docs/README.md

@@ -169,12 +169,12 @@ Follow the [quick start Jupyter Notebook](./samples/product_recommendation_demo.
 
 ## 🚀 Roadmap
 
-For a complete roadmap with estimated dates, please [visit this page](https://github.com/linkedin/feathr/milestones?direction=asc&sort=title&state=open).
-
-- [x] Support streaming
-- [x] Support common data sources
+- [x] Support streaming features with transformation
+- [x] Support common data sources and sinks
 - [x] Support feature store UI, including Lineage and Search functionalities
+- [ ] Support a sandbox Feathr environment for better getting started experience
 - [ ] Support online transformation
+- [ ] More online client libraries such as Java
 - [ ] Support feature versioning
 - [ ] Support feature monitoring
 - [ ] Support feature data deletion and retention

docs/how-to-guides/model-inference-with-feathr.md

@@ -0,0 +1,58 @@
+---
+layout: default
+title: Online Model Inference with Features from Feathr
+parent: How-to Guides
+---
+
+# Online Model Inference with Features from Feathr
+
+After you have materialized features in online store such as Redis, usually end users want to consume those features in production environment for model inference.
+
+With Feathr's online client, it is quite straightforward to do that. The sample code is as below, where users only need to configure the online store endpoint (if using Redis), and call `client.get_online_features()` to get the features for a particular key.
+
+```python
+
+## put the section below into the initialization handler
+import os
+from feathr import FeathrClient
+
+# Set Redis endpoint
+os.environ['online_store__redis__host'] = "xx.redis.cache.windows.net"
+os.environ['online_store__redis__port'] = "6380"
+os.environ['online_store__redis__ssl_enabled'] = "True"
+os.environ['REDIS_PASSWORD'] = "<put-your-key-here>"
+
+client = FeathrClient()
+
+
+# put this section in the model inference handler
+feature = client.get_online_features(feature_table="nycTaxiCITable",
+                                 key='2020-04-15',
+                                 feature_names=['f_is_long_trip_distance', 'f_day_of_week'])
+# `res` will be an array representing the features of that particular key.
+
+
+# `model` will be a ML model that is loaded previously.
+result = model.predict(feature)
+```
+
+## Best Practices
+
+Usually for ML platforms such as Azure Machine Learning, Sagemaker, or DataRobot, there are options where you can "bring your own container" or using "container inference". Basically it requires end users to write an "entry script" and provide a few functions. In those cases, there are usually two handlers:
+
+- an initialization handler to allow users to load configurations. For example, in Azure Machine Learning, it is a function called `init()`, and in Sagemaker, it is `model_fn()`.
+- a model inference handler to do the model inference. For example, in Azure Machine Learning, it is called `init()`, and in Sagemaker, it is called `predict_fn()`.
+
+In the initialization handler, initialize the environment variables and initialize `FeathrClient` as shown in the above script; in the inference handler, call this line:
+
+```python
+# put this section in the model inference handler
+feature = client.get_online_features(feature_table="nycTaxiCITable",
+                                 key='2020-04-15',
+                                 feature_names=['f_is_long_trip_distance', 'f_day_of_week'])
+# `res` will be an array representing the features of that particular key.
+
+
+# `model` will be a ML model that is loaded previously.
+result = model.predict(feature)
+```