just some doc updates

docs/api_classes/overview.md

@@ -1,17 +1,15 @@
 # Overview
 
-!!! tip "Just Getting Started?"
+!!! tip inline end "Just Getting Started?"
     You're in the right place, the SageWorks API Classes are the best way to get started with SageWorks!
 
 Welcome to the SageWorks API Classes
 
+Diagram/image of the classes here
+
 These class provide high-level APIs for the SageWorks package, offering easy access to its core classes:
 
 - **DataSource:** Manages AWS Data Catalog and Athena
 - **FeatureSet:** Manages AWS Feature Store and Feature Groups
 - **Model:** Manages the training and deployment of AWS Model Groups and Packages
 - **Endpoint:** Manages the deployment and invocations/inference on AWS Endpoints
-
-
-!!! note "Not Finding a particular method?"
-    The SageWorks API Classes use 'Core' Classes Internally, so you can lookup all the methods in [SageWorks Core Classes](../core_classes/overview.md)

docs/core_classes/overview.md

@@ -1,14 +1,28 @@
 # Core Classes
 
-!!! warning "The SageWorks Core Classes provide more control but are more complex"
-    These classes interact with many of the AWS service details. They try to balance usablility while maintaining a level of control and refinement over the AWS ML Pipline. For user oriented classes please see [SageWorks API Classes](../api_classes/overview.md)
+!!! warning inline end "SageWorks Core Classes"
+    These classes interact with many of the AWS service details and are therefore more complex. They try to balance usablility while maintaining a level of control and refinement over the AWS ML Pipline.
 
 Welcome to the SageWorks Core Classes
 
-These class provide low-level APIs for the SageWorks package, these classes directly interface with the AWS Sagemaker Pipeline interfaces and have a large number of methods with some complexity.
+The Core Classes provide low-level APIs for the SageWorks package, these classes directly interface with the AWS Sagemaker Pipeline interfaces and have a large number of methods with reasonable complexity.
+
+
+
+For most users the [API Classes](../api_classes/overview.md) should provide the general functionality of creating a full AWS ML Pipeline but if you need more control feel free to investigate and use the Core Classes.
 
 ## Artifacts
-[SageWorks Artifact Classes](artifacts/artifacts.md)
+Artifacts classes provide object oriented interfaces to sets of AWS Services. For instance the [FeatureSetCore](../artifacts/artifacts/#sageworks.core.artifacts.FeatureSetCore) encapsulates and interacts with AWS Feature Store, Feature Groups, Athena, and Data Catalogs.
+
+For a full overview of the Artifact Core Class see
+[Artifact Classes](artifacts/artifacts.md)
 
 ## Transforms
-[SageWorks Artifact Classes](transforms/overview.md)
+Transforms are a set of classes that **transform** one type of `Artifact` to another type. For instance `DataToFeatureSet` takes a `DataSource` artifact and creates a `FeatureSet` artifact.
+
+For a full overview of the Transform Core Class see
+[Transform Classes](transforms/overview.md)
+
+
+
+

docs/misc/general_info.md

@@ -1,7 +1,4 @@
-<img align="left" src="docs/images/scp.png" width="180">
-
-# SageWorks<sup><i>TM</i></sup>
-
+## General Info
 <p align="center">
 <img width="720" alt="DataSource_EDA" aligh="right" src="https://github.com/SuperCowPowers/sageworks/assets/4806709/a5b063f7-a291-4dba-a766-b2e906920066">
 </p>
@@ -10,9 +7,7 @@
 
 SageWorks is a medium granularity framework that manages and aggregates AWS® Services into classes and concepts. When you use SageWorks you think about **DataSources**, **FeatureSets**, **Models**, and **Endpoints**. Underneath the hood those classes handle all the details around updating and managing a **complex set of AWS Services**. All the power and none of the pain so that your team can **Do Science Faster!**
 
-<p align="center">
-<img width="800" src="docs/images/sageworks_concepts.png">
-</p>
+
 
 ### SageWorks Documentation
 See our Python API and AWS documentation here: [SageWorks Documentation](https://supercowpowers.github.io/sageworks/)
@@ -22,7 +17,6 @@ See our Python API and AWS documentation here: [SageWorks Documentation](https:/
 
 
 ## Why SageWorks?
-<img align="right" src="docs/images/graph_representation.png" width="300">
 
 - The AWS SageMaker® ecosystem is **awesome** but has a large number of services with significant complexity
 - SageWorks provides **rapid prototyping** through easy to use **classes** and **transforms**
@@ -36,9 +30,7 @@ See our Python API and AWS documentation here: [SageWorks Documentation](https:/
 ### Single Pane of Glass
 Visibility into the AWS Services that underpin the SageWorks Classes. We can see that SageWorks automatically tags and tracks the inputs of all artifacts providing 'data provenance' for all steps in the AWS modeling pipeline.
 
-<p align="center">
-<img width="800" alt="Top Dashboard" src="https://github.com/SuperCowPowers/sageworks/assets/4806709/c4a7f054-e640-407c-9e5c-f9d3ea1bd717.png">
-</p>
+Image TBD
 
 <i><b> Clearly illustrated:</b> SageWorks provides intuitive and transparent visibility into the full pipeline of your AWS Sagemaker Deployments.</i>
 
@@ -60,7 +52,7 @@ Visibility into the AWS Services that underpin the SageWorks Classes. We can see
 - **Quick prototypes** are typically built with the **light path** and then flipped to the **heavy path** as the system matures and usage grows.
 
 ### Classes and Concepts
-The SageWorks Classes are organized to work in concert with AWS Services. For more details on the current classes and class hierarchies see [SageWorks Classes and Concepts](docs/sageworks_classes_concepts.md).
+The SageWorks Classes are organized to work in concert with AWS Services. For more details on the current classes and class hierarchies see [SageWorks Classes and Concepts](sageworks_classes_concepts.md).
 
 ### Contributions
 If you'd like to contribute to the SageWorks project, you're more than welcome. All contributions will fall under the existing project [license](https://github.com/SuperCowPowers/sageworks/blob/main/LICENSE). If you are interested in contributing or have questions please feel free to contact us at [[email protected]](mailto:[email protected]).