Adds links to Hamilton UI

README.md

@@ -21,6 +21,7 @@
 # Hamilton
 
 The general purpose micro-orchestration framework for building [dataflows](https://en.wikipedia.org/wiki/Dataflow) from python functions. Express data, ML, LLM pipelines/workflows, and web requests in a simple declarative manner.
+Hamilton also comes with a [UI](https://hamilton.dagworks.io/en/latest/concepts/ui) to visualize, catalog, and monitor your dataflows.
 
 Hamilton is a novel paradigm for specifying a flow of delayed execution in python. It works on python objects of any type and dataflows of any complexity. Core to the design of Hamilton is a clear mapping of function name to artifact, allowing you to quickly grok the relationship between the code you write and the data you produce.
 
@@ -32,12 +33,12 @@ Note: Hamilton describes DAGs. If you're looking for something to handle loops o
 ✅ Model a dataflow -- If you can model your problem as a DAG in python, Hamilton is the cleanest way to build it.<br/>
 ✅ Unmaintainable spaghetti code -- Hamilton dataflows are unit testable, self-documenting, and provide lineage.<br/>
 ✅ Long iteration/experimentation cycles -- Hamilton provides a clear, quick, and methodical path to debugging/modifying/extending your code.<br/>
-✅ Reusing code across contexts -- Hamilton encourages code that is independent of infrastructure and can run regardless of execution setting.
+✅ Reusing code across contexts -- Hamilton encourages code that is independent of infrastructure and can run regardless of execution setting.<br/>
+✅ Collaborating on dataflows & tracking execution + artifacts -- Hamilton comes with an optional [UI](https://hamilton.dagworks.io/en/latest/concepts/ui) to visualize, catalog, and monitor your dataflows, which helps team operate smoothly.
 
 ## Problems Hamilton Does not Solve
 ❌ Provisioning infrastructure -- you want a macro-orchestration system (see airflow, kubeflow, sagemaker, etc...).<br/>
 ❌ Doing your ML for you -- we organize your code, BYOL (bring your own libraries).<br/>
-❌ Tracking execution + associated artifacts -- Hamilton is lightweight, but if this is important to you see the [DAGWorks product](https://www.dagworks.io).
 
 See the table below for more specifics/how it compares to other common tooling.
 

docs/concepts/ui.rst

@@ -5,9 +5,10 @@ Hamilton UI
 Hamilton comes with a fully open-source UI that can be run both for local deployment and on a remote server.
 The UI consists of the following features:
 
-1. Telemetry for hamilton executions -- both on the history of executions and the data itself
-2. A feature/artifact catalog for browsing/connecting executions of nodes -> results
-3. A DAG visualizer for exploring the DAG, looking at the code, and determining lineage
+1. Telemetry for hamilton executions -- both on the history of executions and the data itself.
+2. A feature/artifact catalog for browsing/connecting executions of nodes -> results.
+3. A dataflow (i.e. DAG) visualizer for exploring and looking at your code, and determining lineage.
+4. A project explorer for viewing curating projects and viewing versions of your Hamilton dataflows.
 
 --------
 Overview

docs/main.md

@@ -6,6 +6,7 @@
 </div>
 
 Hamilton is a general-purpose framework to write dataflows using regular Python functions. At the core, each function defines a transformation and its parameters indicates its dependencies. Hamilton automatically connects individual functions into a [Directed Acyclic Graph](https://en.wikipedia.org/wiki/Directed_acyclic_graph) (DAG) that can be executed, visualized, optimized, and reported on.
+Hamilton also comes with a [UI](concepts/ui.rst) to visualize, catalog, and monitor your dataflows.
 
 ```{figure} ./_static/abc.png
 :scale: 50
@@ -16,6 +17,7 @@ The ABC of Hamilton
 
 # Why should you use Hamilton?
 **Facilitate collaboration**. By focusing on functions, Hamilton avoids sprawling code hierarchy and generates flat dataflows. Well-scoped functions make it easier to add features, complete code reviews, debug pipeline failures, and hand-off projects. Visualizations can be generated directly from your code to better understand and document it.
+Integration with the [Hamilton UI](concepts/ui.rst) allows you to track lineage, catalog code & artifacts, and monitor your dataflows.
 
 **Reduce development time**. Hamilton dataflows are reusable across projects and context (e.g., pipeline vs. web service). The benefits of developing robust and well-tested solutions are multiplied by reusability. Off-the-shelf dataflows are available on the [Hamilton Hub](https://hub.dagworks.io/).
 
@@ -71,3 +73,5 @@ Multiple companies are doing cool stuff with Hamilton! Come chat with members of
 * **Lexis Nexis** - Feature processing and lineage
 * **Opendoor** - Manage PySpark pipelines
 * **KI** - Feature engineering
+* **AB-InBev** - Machine learning Platform on PySpark
+* **Rippling** - Feature engineering on PySpark