#5 📝 Tranche 2 of documentation migration

README.md

@@ -43,7 +43,7 @@ processes adjusted and honed into the public GitHub
 
 ## Configurations
 
-For details on using the configuration tool, please consult our [Configuring Your Environment guidance (LINK COMING SOON)](https://boozallen.github.io/aissemble/current-dev/configurations.html).
+For details on using the configuration tool, please consult our [Configuring Your Environment guidance](https://boozallen.github.io/aissemble/aissemble/current/configurations.html).
 
 ## Build
 
@@ -76,7 +76,7 @@ following profiles are often useful when first starting with aiSSEMBLE:
   build does not build the Docker images directly. The images are built within the Kubernetes cluster to speed up
   development builds and save disk space.**
 
-## Use a Maven Archetype to Create a New aiSSEMBLE-Based  Project
+## Use a Maven Archetype to Create a New aiSSEMBLE-Based Project
 
 The first step in creating a new project is to leverage Maven’s archetype functionality to incept a new Maven project
 that will contain all of your aiSSEMBLE component implementations - Data Delivery and Machine Learning pipelines as
@@ -97,7 +97,7 @@ This command will trigger an interactive questionnaire giving you the opportunit
 5. projectGitUrl
 6. projectName
 
-* For details on these fields refer to (LINK COMING SOON) https://boozallen.github.io/aissemble/current-dev/archetype.html#_use_a_maven_archetype_to_create_a_new_project
+* For details on these fields refer to https://boozallen.github.io/aissemble/aissemble/current/archetype.html
 
 * For detailed instructions on adding a pipeline refer to (LINK COMING SOON) https://boozallen.github.io/aissemble/current-dev/add-pipelines-to-build.html
 

docs/modules/ROOT/pages/add-pipelines-to-build.adoc

@@ -0,0 +1,62 @@
+[#_adding_a_pipeline]
+= Adding a Pipeline
+
+Once you have generated your new project, it is time to add a pipeline. Pipelines are the core of most projects and are
+responsible for major data delivery and machine learning tasks. The following content walks through the process of
+standing up a pipeline at a very high level.
+
+=== Step 1: Creating the pipeline model file
+aiSSEMBLE(TM) uses Model Driven Architecture (MDA) to accelerate development. Pipeline models are JSON files used to
+drive the generation of multiple aspects in your project - including the pipeline code module and deployment modules.
+
+. Create a new JSON file in your project's `pipeline-model` directory.
+** Sample path: `test-project/test-project-pipeline-models/src/main/resources/pipelines` +
+. Create the model pipeline data within the newly added JSON file. For detailed options, see the pipeline
+xref:pipeline-metamodel.adoc[metamodel documentation].
+.. *Note:* Ensure the name of the JSON file matches the `"name"` of the pipeline.
+
+*_Example:* Shown below is the SimpleDataDeliveryExample pipeline._
+
+._View SimpleDataDeliveryExample.json_
+[%collapsible]
+====
+[source]
+----
+{
+   "name":"SimpleDataDeliveryExample",
+   "package":"com.boozallen.aissemble.documentation",
+   "type":{
+      "name":"data-flow",
+      "implementation":"data-delivery-spark"
+   },
+   "steps":[
+      {
+         "name":"IngestData",
+         "type":"synchronous",
+         "dataProfiling":{
+            "enabled":false
+         }
+      }
+   ]
+}
+----
+====
+
+=== Step 2: Generating the pipeline code
+After creating your model pipeline, execute the build to trigger the creation of the Maven modules that accompany it.
+
+. Run the maven build to execute the MDA generation engine.
+.. `mvn clean install`
+
+. The MDA generator will take several build iterations to fully generate your project, and requires that you modify
+certain files to enable this generation. These *Manual Actions* are meant to guide you through that process, and will
+only need to be performed after changes to your pipeline model(s).
+[source]
+----
+***********************************************************************
+*** MANUAL ACTION NEEDED!                                           ***
+***********************************************************************
+----
+
+[start=3]
+. Re-run the build and address all manual actions until they have been resolved.

docs/modules/ROOT/pages/aissemble-approach.adoc

@@ -0,0 +1,38 @@
+= aiSSEMBLE(TM) Approach
+
+aiSSEMBLE is a lean manufacturing approach for holistically designing, developing, and fielding AI. The aiSSEMBLE Baseline
+is a framework which builds on the software and machine learning engineering best practices and lessons learned,
+providing and maintaining reusable components, rather than a one-size-fits-all platform. In return, various members of
+your team, including solution architects, DevOps engineers, data/software engineers, and AI/ML practitioners can easily
+leverage these components to suit your business needs and integrate with any other tools that meet the needs of your
+project.
+
+== Baseline Fabrications
+The Baseline represents the Factory component in the diagram below. aiSSEMBLE leverages
+xref:aissemble-approach.adoc#_configurable_tooling[configurable tooling] in the fabrication process to generate key
+scaffolding and other components that are tailored to your project. aiSSEMBLE also establishes a build process that
+produces deployment-ready artifacts.
+
+image::aissemble-solution-architecture.png[align="left",width=1366,height=768]
+
+
+[#_configurable_tooling]
+=== Configurable Tooling
+With the fast-moving landscape of tools and techniques within AI, a process that can quickly change at the speed of AI
+is needed. aiSSEMBLE understands it is crucial that the nuances of existing customer environments and preferences can be
+incorporated into AI projects as an integral concept. The Baseline’s Configurable Tooling is realized by using
+https://www.omg.org/mda/[Model Driven Architecture,role=external,window=_blank] to describe your AI system with simple
+JSON files. These files are then ingested to generate Python, Java, Scala, or any other type of file. Configurable
+tooling exists for a vast array of components leveraging popular implementation choices, which can be used as-is or
+tailored to specific needs.
+
+=== Reusable Components
+Reusable components represent the "ready-to-deploy" artifacts within aiSSEMBLE. These reusable components can be used in
+a project directly. These include, but are not limited to, data lineage, AI model versioning, and bias detection.
+
+== aiSSEMBLE Landscape
+aiSSEMBLE is not a platform, but rather a framework that integrates with various technologies. The graphic below
+demonstrates the representative set of technologies aiSSEMBLE integrates with. This set is always evolving to
+keep pace with the rapidly changing AI space.
+
+image::aissemble-landscape.png[align="left",width=1366,height=768]

docs/modules/ROOT/pages/aissemble-versions.adoc

@@ -7,5 +7,5 @@ https://github.com/boozallen/aissemble/releases[Releases page,role=external,wind
 ## Preview of Next Release
 
 Given the fragile nature of GitHub Release Notes (no version control), we track our in-flight changes in a vesioned
-document within the codebase.  Check out https://github.com/boozallen/aissemble/releases[DRAFT Release Notes,role=external,window=_blank]
-to see what's coming in the next release.
+document within the codebase.  Check out https://github.com/boozallen/aissemble/blob/dev/DRAFT_RELEASE_NOTES.md[DRAFT
+Release Notes,role=external,window=_blank] to see what's coming in the next release.

docs/modules/ROOT/pages/index.adoc

@@ -9,24 +9,24 @@ GitHub issue,role=external,window=_blank] for more information.
 * **aiSSEMBLE** – (pronounced assemble) is Booz Allen’s lean, manufacturing-inspired approach for holistically designing,
 developing, and fielding AI solutions across the engineering lifecycle: from data processing (DataOps) to model building,
 tuning, and training (ModelOps, MLOps), to secure operational deployment (DevSecOps).
-* **https://github.com/boozallen/aissemble[Solution Baseline,role=external,window=_blank]** - An evolving,
+* **https://github.com/boozallen/aissemble[aiSSEMBLE Baseline,role=external,window=_blank]** - An evolving,
 versioned, core set of capabilities that can serve as an implementation's foundation, enabling the rapid standup of an
 operational data delivery, data preparation, and/or machine learning (ML) pipelines.
 
 == aiSSEMBLE Overview
 [.lead]
-The aiSSEMBLE Solution Baseline is an accelerant that helps AI projects by leveraging pre-packaged and tailored
+The aiSSEMBLE Baseline is an accelerant that helps AI projects by leveraging pre-packaged and tailored
 components, allowing a shift in focus towards value-added tasks, rather than labor-intensive, boilerplate tasks.
 
-The Solution Baseline also lowers the barrier for including features that are otherwise treated as nice-to-have items,
+The Baseline also lowers the barrier for including features that are otherwise treated as nice-to-have items,
 as teams often do not have time to incorporate them (e.g., drift detection, bias detection, provenance capture).
 
-The Solution Baseline is the key enabler for the aiSSEMBLE manufacturing process: a modern take on a holistic,
+The Baseline is the key enabler for the aiSSEMBLE manufacturing process: a modern take on a holistic,
 integrated lean software manufacturing process that can deliver a variety of AI products at high velocity. The graphic
 below demonstrates the steps to kick off an aiSSEMBLE project.
 
 // .aiSSEMBLE Notional Architecture
-image::solution-baseline-process-overview.png[align="center",alt="Solution Baseline Overview",width=1366,height=768]
+image::solution-baseline-process-overview.png[align="center",alt="Baseline Overview",width=1366,height=768]
 
 
 == New project with aiSSEMBLE