Update README.md (#1175)

(cherry picked from commit 6de67ad)

Author: deliahu

README.md

@@ -1,146 +1,50 @@
 # Build machine learning APIs
 
-<br>
-
-<!-- Set header Cache-Control=no-cache on the S3 object metadata (see https://help.github.com/en/articles/about-anonymized-image-urls) -->
-![Demo](https://d1zqebknpdh033.cloudfront.net/demo/gif/v0.13_2.gif)
+Cortex makes deploying, scaling, and managing machine learning systems in production simple. We believe that developers in any organization should be able to add natural language processing, computer vision, and other machine learning capabilities to their applications without having to worry about infrastructure.
 
 <br>
 
 ## Key features
 
-* **Multi framework:** deploy TensorFlow, PyTorch, scikit-learn, and other models.
-* **Autoscaling:** automatically scale APIs to handle production workloads.
-* **ML instances:** run inference on G4, P2, Inf1, M5, C5 and other AWS instance types.
-* **Spot instances:** save money with spot instances.
-* **Multi-model APIs:** deploy multiple models in a single API.
-* **Rolling updates:** update deployed APIs with no downtime.
-* **Log streaming:** stream logs from deployed models to your CLI.
-* **Prediction monitoring:** monitor API performance and prediction results.
-
-<br>
-
-## Deploying a model
+### Deploy
 
-### Install the CLI
+* Run Cortex locally or as a production cluster on your AWS account.
+* Deploy TensorFlow, PyTorch, scikit-learn, and other models as web APIs.
+* Define preprocessing and postprocessing steps in Python.
 
-<!-- CORTEX_VERSION_README_MINOR -->
-```bash
-$ bash -c "$(curl -sS https://raw.githubusercontent.com/cortexlabs/cortex/0.18/get-cli.sh)"
-```
-
-### Implement your predictor
+### Manage
 
-```python
-# predictor.py
-
-class PythonPredictor:
-    def __init__(self, config):
-        self.model = download_model()
-
-    def predict(self, payload):
-        return self.model.predict(payload["text"])
-```
+* Update APIs with no downtime.
+* Stream logs from your APIs to your CLI.
+* Monitor API performance and track predictions.
 
-### Configure your deployment
+### Scale
 
-```yaml
-# cortex.yaml
-
-- name: sentiment-classifier
-  predictor:
-    type: python
-    path: predictor.py
-  compute:
-    gpu: 1
-    mem: 4G
-```
-
-### Deploy your model
-
-```bash
-$ cortex deploy
-
-creating sentiment-classifier
-```
-
-### Serve predictions
-
-```bash
-$ curl http://localhost:8888 \
-    -X POST -H "Content-Type: application/json" \
-    -d '{"text": "serving models locally is cool!"}'
-
-positive
-```
+* Automatically scale APIs to handle production traffic.
+* Reduce your cloud infrastructure spend with spot instances.
+* Maximize resource utilization by deploying multiple models per API.
 
 <br>
 
-## Deploying models at scale
-
-### Spin up a cluster
+## How it works
 
-Cortex clusters are designed to be self-hosted on any AWS account:
-
-```bash
-$ cortex cluster up
-
-aws region: us-east-1
-aws instance type: g4dn.xlarge
-spot instances: yes
-min instances: 0
-max instances: 5
-
-your cluster will cost $0.19 - $2.85 per hour based on cluster size and spot instance pricing/availability
-
-￮ spinning up your cluster ...
-
-your cluster is ready!
-```
-
-### Deploy to your cluster with the same code and configuration
-
-```bash
-$ cortex deploy --env aws
-
-creating sentiment-classifier
-```
-
-### Serve predictions at scale
+<!-- Set header Cache-Control=no-cache on the S3 object metadata (see https://help.github.com/en/articles/about-anonymized-image-urls) -->
+![Demo](https://d1zqebknpdh033.cloudfront.net/demo/gif/v0.13_2.gif)
 
-```bash
-$ curl http://***.amazonaws.com/sentiment-classifier \
-    -X POST -H "Content-Type: application/json" \
-    -d '{"text": "serving models at scale is really cool!"}'
+<br>
 
-positive
-```
+## Get started
 
-### Monitor your deployment
+### Install
 
+<!-- CORTEX_VERSION_README_MINOR -->
 ```bash
-$ cortex get sentiment-classifier
-
-status   up-to-date   requested   last update   avg request   2XX
-live     1            1           8s            24ms          12
-
-class     count
-positive  8
-negative  4
+bash -c "$(curl -sS https://raw.githubusercontent.com/cortexlabs/cortex/0.18/get-cli.sh)"
 ```
 
-### How it works
-
-The CLI sends configuration and code to the cluster every time you run `cortex deploy`. Each model is loaded into a Docker container, along with any Python packages and request handling code. The model is exposed as a web service using a Network Load Balancer (NLB) and FastAPI / TensorFlow Serving / ONNX Runtime (depending on the model type). The containers are orchestrated on Elastic Kubernetes Service (EKS) while logs and metrics are streamed to CloudWatch.
-
-Cortex manages its own Kubernetes cluster so that end-to-end functionality like request-based autoscaling, GPU support, and spot instance management can work out of the box without any additional DevOps work.
-
-<br>
+<!-- CORTEX_VERSION_README_MINOR -->
+See our [installation guide](https://docs.cortex.dev/install), then deploy one of our [examples](https://github.com/cortexlabs/cortex/tree/0.18/examples) or bring your own models to build [custom APIs](https://docs.cortex.dev/deployments/exporting).
 
-## Examples
+### Learn more
 
-<!-- CORTEX_VERSION_README_MINOR x4 -->
-* [Image classification](https://github.com/cortexlabs/cortex/tree/0.18/examples/tensorflow/image-classifier): deploy an Inception model to classify images.
-* [Search completion](https://github.com/cortexlabs/cortex/tree/0.18/examples/pytorch/search-completer): deploy Facebook's RoBERTa model to complete search terms.
-* [Text generation](https://github.com/cortexlabs/cortex/tree/0.18/examples/pytorch/text-generator): deploy Hugging Face's DistilGPT2 model to generate text.
-* See [all examples](https://github.com/cortexlabs/cortex/tree/0.18/examples)
+Check out our [docs](https://docs.cortex.dev) and join our [community](https://gitter.im/cortexlabs/cortex).