Merge branch 'getting_started_docs' of https://github.com/mobiusml/aa…

…na_sdk into getting_started_docs

docs/aana_intro.md

@@ -1,11 +1,11 @@
 # Aana SDK
 
-Aana is a multi-modal SDK for deploying and serving machine learning models.
+Aana SDK is a SDK for deploying and serving multimodal machine learning models of Mobius Labs.
 
 The SDK is designed according to the following principles:
 - **Reliability**: Aana is designed to be reliable and robust. It is built to be fault-tolerant and to handle failures gracefully.
 - **Scalability**: Aana is designed to be scalable. It is built on top of Ray, a distributed computing framework, and can be easily scaled to multiple servers.
-- **Efficiency**: Aana is designed to be efficient. It is built to be fast, parallel, and to use resources efficiently. It is built on top of Ray and Mobius Pipeline, a Python package designed for efficient and parallel processing of complex workflows.
+- **Efficiency**: Aana is designed to be efficient. It is built to be fast, parallel, and to use resources efficiently. 
 - **Easy to Use**: Aana is designed to be easy to use by developers. It is built to be modular, with a lot of automation and abstraction.
 
 These design principles are reflected in the architecture of the SDK. The SDK is built on top of Ray, a distributed computing framework, and Mobius Pipeline, a Python package designed for efficient and parallel processing of complex workflows. The SDK is designed to be modular, with a lot of automation and abstraction.
@@ -30,8 +30,8 @@ Here are some key capabilities of Mobius Pipeline:
 
 Mobius Pipeline can handle different types of computational tasks: Ray tasks, Ray Deployments, and general Python functions. 
 
-- **Ray Deployment**: A Ray Deployment is used for the nodes that need to keep the state in memory between calls, such as model serving where the model needs to be loaded only once and then used for multiple requests.
 - **Ray Task**: A Ray Task is used for the nodes that are computationally intensive and can be parallelized. Ray Tasks are executed in parallel as a separate Python process.
+- **Ray Deployment**: A Ray Deployment is used for the nodes that need to keep the state in memory between calls, such as model serving where the model needs to be loaded only once and then used for multiple requests.
 - **General Python Function**: A general Python function is used for the nodes that are simple, like I/O tasks, and don't need much computational resources. This nodes are executed in the same Python process as the rest of the pipeline.
 
 Let's take a look at the example: