Update Readme.rst

fix syntax

Author: sokolegg

Readme.rst

@@ -48,8 +48,8 @@ Comparison to related technologies
 
 #. **Jupyter** - is the de facto experimental environment for most data scientists. However, it is desirable to write experimental code.
 
-#. **Data Engineering tools such as** `**AirFlow** <https://airflow.apache.org/>`_,
-   `**Luigi** <https://github.com/spotify/luigi>`_-These are very popular ML pipeline build tools. Airflow can be connected to a kubernetes cluster or collect tasks through a simple PythonOperator. The downside is that their functionality is generally limited on this, that is, they do not provide ML modules out of the box. Moreover, all developments will still have to be wrapped in a scheduler and this is not always a trivial task. However, we like them and we use Airflow and Luigi as possible context for executors.
+#. **Data Engineering tools such as** `AirFlow <https://airflow.apache.org/>`_ or
+   `Luigi <https://github.com/spotify/luigi>`_-These are very popular ML pipeline build tools. Airflow can be connected to a kubernetes cluster or collect tasks through a simple PythonOperator. The downside is that their functionality is generally limited on this, that is, they do not provide ML modules out of the box. Moreover, all developments will still have to be wrapped in a scheduler and this is not always a trivial task. However, we like them and we use Airflow and Luigi as possible context for executors.
 
 #. **Azure ML / Amazon SageMaker / Google Cloud** - Cloud platforms really allow you to assemble an entire system from ready-made modules and put it into operation relatively quickly. Of the minuses: high cost, binding to a specific cloud, as well as small customization for specific business needs. For a large business, this is the most logical option - to build an ML infrastructure in the cloud. We also maintain cloud options as posible ways for the deployment step.