Adds deployment cookbook with a set of examples/instructions

It also has a few issues that function as placeholders. We'll want to
centralize this more as we get more tooling in Burr itself.

Note this deletes a few files that were duplicates of READMEs in the
examples.

These:
- https://github.com/DAGWorks-Inc/burr/tree/a1a0b3bcb0f64790615042527c0e173a6c436083/examples/deployment/aws/lambda
- https://github.com/DAGWorks-Inc/burr/tree/main/examples/web-server

docs/contributing/contributing.rst

@@ -1,3 +1,5 @@
+.. _contributing:
+
 ============
 Contributing
 ============

docs/examples/deployment/infrastructure.rst

@@ -0,0 +1,8 @@
+-------------------------------------
+Provisioning Infrastructure/Deploying
+-------------------------------------
+
+Burr is not opinionated about the method of deployment/cloud one uses. Any method that can run a python server will work
+(AWS, vercel, etc...). Note we aim to have more examples here -- see `this issue <https://github.com/DAGWorks-Inc/burr/issues/390>`_ to track!
+
+- `Deploying Burr in an AWS lambda function <https://github.com/DAGWorks-Inc/burr/tree/main/examples/deployment/aws/lambda>`_

docs/examples/deployment/monitoring.rst

@@ -0,0 +1,24 @@
+------------------------
+Monitoring in Production
+------------------------
+
+Burr's telemetry UI is meant both for debugging and running in production. It can consume `OpenTelemetry traces <https://burr.dagworks.io/reference/integrations/opentelemetry/>`_,
+and has a suite of useful capabilities for debugging Burr applications.
+
+It has two (current) implementations:
+
+1. `Local (filesystem) tracking <https://burr.dagworks.io/concepts/tracking/>`_ (default, for debugging or lower-scale production use-cases with a distributed file-system)
+2. `S3-based tracking <https://github.com/DAGWorks-Inc/burr/blob/main/burr/tracking/server/s3/README.md>`_ (meant for production use-cases)
+
+Which each come with an implementation of data storage on the server.
+
+To deploy these in production, you can follow the following examples:
+
+1. `Burr + FastAPI + docker <https://github.com/mdrideout/burr-fastapi-docker-compose>`_ by `Matthew Rideout <https://github.com/mdrideout>`_. This contains a sample API + UI + tracking server all bundled in one!
+2. `Docker compose + nginx proxy <https://github.com/DAGWorks-Inc/burr/tree/main/examples/email-assistant#running-the-ui-with-email-server-backend-in-a-docker-container>`_ by `Aditha Kaushik <https://github.com/97k>`_ for the email assistant example, demonstrates running the docker image with the tracking server.
+
+We also have a few issues to document deploying Burr's monitoring system in production:
+
+- `deploy on AWS <https://github.com/DAGWorks-Inc/burr/issues/391>`_
+- `deploy on GCP <https://github.com/DAGWorks-Inc/burr/issues/392>`_
+- `deploy on Azure <https://github.com/DAGWorks-Inc/burr/issues/393>`_

docs/examples/deployment/web-server.rst

@@ -0,0 +1,22 @@
+--------------------
+Burr in a web server
+--------------------
+
+We largely use `fastAPI <https://fastapi.tiangolo.com/>`_ as our web server, but Burr can work with any python-friendly server framework
+(`django <https://www.djangoproject.com>`_, `flask <https://flask.palletsprojects.com/>`_, etc...).
+
+To run Burr in a FastAPI server, see the following examples:
+
+- `Human in the loop FastAPI server <https://github.com/DAGWorks-Inc/burr/tree/main/examples/web-server>`_ (`TDS blog post <https://towardsdatascience.com/building-an-email-assistant-application-with-burr-324bc34c547d>`__ )
+- `OpenAI-compatible agent with FastAPI <https://github.com/DAGWorks-Inc/burr/tree/main/examples/openai-compatible-agent>`_
+- `Streaming server using SSE + FastAPI <https://github.com/DAGWorks-Inc/burr/tree/main/examples/streaming-fastapi>`_ (`TDS blog post <https://towardsdatascience.com/how-to-build-a-streaming-agent-with-burr-fastapi-and-react-e2459ef527a8>`__ )
+- `Use typed state with Pydantic + FastAPI <https://github.com/DAGWorks-Inc/burr/tree/main/examples/typed-state>`_
+
+Connecting to a database
+------------------------
+
+To connect Burr to a database, you can use one of the provided persisters, or build your own:
+
+- :ref:`Documentation on persistence <state-persistence>`
+- :ref:`Set of available persisters <persistersref>`
+- `Simple chatbot intro with persistence to SQLLite <https://github.com/DAGWorks-Inc/burr/blob/main/examples/simple-chatbot-intro/notebook.ipynb>`_