Update README.md

README.md

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 [![codecov](https://codecov.io/gh/iterative/mlem/branch/main/graph/badge.svg?token=WHU4OAB6O2)](https://codecov.io/gh/iterative/mlem)
 [![PyPi](https://img.shields.io/pypi/v/mlem.svg?label=pip&logo=PyPI&logoColor=white)](https://pypi.org/project/mlem)
 
-MLEM helps you with model deployment. It saves ML models in a standard format that can be used in a variety of downstream deployment scenarios such as real-time serving through a REST API or batch processing. MLEM format is a human-readable text that helps you use GitOps with Git as the single source of truth.
+MLEM helps you package and deploy machine learning models.
+It saves model metadata in a standard, human-readable format that can be used in a variety of deployment scenarios, such as real-time serving through a REST API or a batch processing task.
+MLEM lets you keep Git as the single source of truth for code and models.
 
-- **Run your model anywhere you want:** package it as a Python package, a Docker Image or deploy it to Heroku (SageMaker, Kubernetes and more platforms are coming). Switch between formats and deployment platforms with a single command thanks to unified abstraction.
-- **Simple text file to save model metadata:** automatically package Python env requirements and input data specifications into a ready-to-deploy format. Use the same human-readable format for any ML framework.
-- **Stick to your training workflow:** MLEM doesn't ask you to rewrite your training code. To start using packaging or deployment machinery, add just two lines to your python script: one to import the library and one to save the model.
-- **Developer-first experience:** use CLI when you feel like DevOps and API when you feel like a developer.
+- **Run your ML models anywhere:**
+  Wrap models as a Python package or Docker Image, or deploy them to Heroku (SageMaker, Kubernetes, and more platforms coming soon).
+  Switch between platforms transparently, with a single command.
 
-## Why MLEM?
+- **Simple text file to save model metadata:**
+  Automatically include Python requirements and input data needs into a deployment-ready format.
+  Use the same format on any ML framework.
 
-The main reason to use MLEM instead of other related solutions is that it works well with **GitOps approach** and helps you manage model lifecycle in Git:
+- **Stick to your training workflow:**
+  MLEM doesn't ask you to rewrite model training code.
+  Just add two lines around your Python code: one to import the library and one to save the model.
 
-- **Git as a single source of truth:** we use plain text to save metadata for models that can be saved and versioned.
-- **Reuse existing Git and Github/Gitlab infrastructure** for model management instead of installing separate model management software.
-- **Unify model and software deployment.** Deploy models using the same processes and code you use to deploy software.
+- **Developer-first experience:**
+  Use the CLI when you feel like DevOps and the API when you feel like a developer.
+
+## Why is MLEM special?
+
+The main reason to use MLEM instead of other tools is to adopt a **GitOps approach**, helping you manage model lifecycles in Git:
+
+- **Git as a single source of truth:** MLEM writes model metadata to a plain text file that can be versioned in a Git repo along with your code.
+- **Unify model and software deployment:** Release models using the same processes used for software updates (branching, pull requests, etc.).
+- **Reuse existing Git infrastructure:** Use familiar hosting like Github or Gitlab for model management, instead of having separate services.
 
 ## Installation
 
-Install MLEM with pip:
+MLEM requires Python 3.
 
-```
-$ pip install mlem
+```console
+$ pyhon -m pip install mlem
 ```
 
-To install the development version, run:
+To install the bleeding-edge version, use:
 
-```
-$ pip install git+https://github.com/iterative/mlem
+```console
+$ pyhon -m pip install git+https://github.com/iterative/mlem
 ```
 
 ## Anonymized Usage Analytics
 
-To help us better understand how MLEM is used and improve it, MLEM captures and reports anonymized usage statistics. You will be notified the first time you run `mlem init`.
+To help us better understand how MLEM is used and improve it, MLEM captures and reports anonymized usage statistics.
 
-### What
 MLEM's analytics record the following information per event:
+
 - MLEM version (e.g., `0.1.2+5fb5a3.mod`) and OS version (e.g., `MacOS 10.16`)
 - Command name and exception type (e.g., `ls, ValueError` or `get, MLEMRootNotFound`)
 - Country, city (e.g., `RU, Moscow`)
 - A random user_id (generated with [uuid](https://docs.python.org/3/library/uuid.html))
 
-### Implementation
-The code is viewable in [analytics.py](https://github.com/iterative/mlem/mlem/analytics.py). They are done in a separate background process and fail fast to avoid delaying any execution. They will fail immediately and silently if you have no network connection.
-
-MLEM's analytics are sent through Iterative's proxy to Google BigQuery over HTTPS.
+> The code is viewable in [analytics.py](https://github.com/iterative/mlem/mlem/analytics.py).
+> MLEM's analytics are sent through Iterative's proxy to Google BigQuery over HTTPS.
 
 ### Opting out
-MLEM analytics help the entire community, so leaving it on is appreciated. However, if you want to opt out of MLEM's analytics, you can disable it via setting an environment variable `MLEM_NO_ANALYTICS=true` or by adding `no_analytics: true` to `.mlem/config.yaml`
 
-This will disable it for the project. We'll add an option to opt out globally soon.
+MLEM analytics have no performance impact and help the entire community, so leaving them on is appreciated.
+However, to opt out set an environment variable `MLEM_NO_ANALYTICS=true` or add `no_analytics: true` to `.mlem/config.yaml`.
+
+> This will disable it for the project.
+> We'll add an option to opt out globally soon.