Update image widths to 40% and fix broken links

Author: htahir1

oncoclear/README.md

@@ -18,7 +18,7 @@ OncoClear is an end-to-end MLOps solution that transforms raw diagnostic measure
 
 <div align="center">
   <br/>
-    <img alt="OncoClear Architecture" src=".assets/architecture_diagram.png" width="70%">
+    <img alt="OncoClear Architecture" src=".assets/architecture_diagram.png" width="40%">
   <br/>
 </div>
 
@@ -34,7 +34,7 @@ The OncoClear project consists of four integrated pipelines:
 
    <div align="center">
      <br/>
-       <img alt="Feature Engineering Pipeline" src=".assets/feature_engineering_pipeline.png" width="60%">
+       <img alt="Feature Engineering Pipeline" src=".assets/feature_engineering_pipeline.png" width="40%">
      <br/>
    </div>
 
@@ -46,7 +46,7 @@ The OncoClear project consists of four integrated pipelines:
 
    <div align="center">
      <br/>
-       <img alt="Training Pipeline" src=".assets/training_pipeline.png" width="60%">
+       <img alt="Training Pipeline" src=".assets/training_pipeline.png" width="40%">
      <br/>
    </div>
 
@@ -57,7 +57,7 @@ The OncoClear project consists of four integrated pipelines:
 
    <div align="center">
      <br/>
-       <img alt="Inference Pipeline" src=".assets/inference_pipeline.png" width="60%">
+       <img alt="Inference Pipeline" src=".assets/inference_pipeline.png" width="40%">
      <br/>
    </div>
 

vertex-registry-and-deployer/README.md

@@ -3,7 +3,7 @@
 
 Welcome to your ZenML project for deploying ML models using Google Cloud's Vertex AI! This project provides a hands-on experience with MLOps pipelines using ZenML and Vertex AI. It contains a collection of ZenML steps, pipelines, and other artifacts to help you efficiently deploy your machine learning models.
 
-Using these pipelines, you can run data preparation, model training, registration, and deployment with a single command while using YAML files for [configuration](https://docs.zenml.io/user-guide/production-guide/configure-pipeline). ZenML takes care of tracking your metadata and [containerizing your pipelines](https://docs.zenml.io/how-to/customize-docker-builds).
+Using these pipelines, you can run data preparation, model training, registration, and deployment with a single command while using YAML files for [configuration](https://docs.zenml.io/user-guides/production-guide/configure-pipeline). ZenML takes care of tracking your metadata and [containerizing your pipelines](https://docs.zenml.io/how-to/customize-docker-builds).
 
 
 ## 🏃 How to run

zenml-support-agent/run.ipynb

@@ -396,7 +396,7 @@
       "\u001b[2;36mwill only take effect when you're running ZenML from the initialized repository \u001b[0m\n",
       "\u001b[2;36mroot, or from a subdirectory. For more information on repositories and \u001b[0m\n",
       "\u001b[2;36mconfigurations, please visit \u001b[0m\n",
-      "\u001b[2;4;94mhttps://docs.zenml.io/user-guide/starter-guide/understand-stacks.\u001b[0m\n"
+      "\u001b[2;4;94mhttps://docs.zenml.io/user-guides/starter-guide/understand-stacks.\u001b[0m\n"
      ]
     }
    ],
@@ -476,7 +476,7 @@
    "metadata": {},
    "source": [
     "#### Stack\n",
-    "A [stack](https://docs.zenml.io/user-guide/production-guide/understand-stacks) in ZenML is the combination of tools and infrastructure that your pipelines can run on. When you run ZenML code without configuring a stack, the pipeline will run on the so-called default stack.\n",
+    "A [stack](https://docs.zenml.io/user-guides/production-guide/understand-stacks) in ZenML is the combination of tools and infrastructure that your pipelines can run on. When you run ZenML code without configuring a stack, the pipeline will run on the so-called default stack.\n",
     "\n",
     "![image.png](attachment:image.png)\n",
     "\n",