From 6e1550c45ebd5cc8d111c0f3856c08dc77961e8e Mon Sep 17 00:00:00 2001
From: Joannes Madu <134279516+joannesmadu@users.noreply.github.com>
Date: Fri, 11 Oct 2024 11:30:35 +0100
Subject: [PATCH] Links bug fix/brid 98 (#9)

* directory cleanup

* directory cleanup

* directory cleanup

* directory cleanup

* directory cleanup

* directory cleanup

* sidebar URL polish

* sidebar URL polish

* sidebar URL polish

* sidebar URL polish

* sidebar URL polish

* sidebar URL polish

* sidebar URL polish

* sidebar URL relative links restored

* sidebar URL relative links amend

* sidebar URL - config.yml amend

* sidebar URL - config.yml amend

* sidebar URL - base URL

* sidebar URL - base URL x relative path amend

* sidebar URL - base URL x relative path amend

* sidebar URL relative path amend

* sidebar URL relative path amend

* ruby security consolidation

* sidebar live links final

* image relative links

* home nav

* relative links across pages

* relative links across pages

* gitignore

* gitignore

* relative link best practices

---------

Co-authored-by: Joannes Madu <joannesmadu@192.168.1.225>
---
 .gitignore                                 |   3 +-
 Gemfile.lock                               | 106 ---------------------
 _layouts/default.html                      |  43 +++++----
 corporate_perspective/best_practices.md    |   8 +-
 index.md                                   |   4 +-
 mlops_big_picture/mlops_summary.md         |   7 +-
 mlops_big_picture/requirements_research.md |  16 ++--
 mlops_big_picture/serving.md               |  14 +--
 mlops_big_picture/team_arch.md             |   3 +-
 mlops_big_picture/versioning.md            |   3 +-
 10 files changed, 54 insertions(+), 153 deletions(-)
 delete mode 100644 Gemfile.lock

diff --git a/.gitignore b/.gitignore
index 314373b..48754b8 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,4 +1,5 @@
 */.DS_Store
 .DS_Store
 _site/
-_site/*
\ No newline at end of file
+_site/*
+Gemfile.lock
\ No newline at end of file
diff --git a/Gemfile.lock b/Gemfile.lock
deleted file mode 100644
index a752938..0000000
--- a/Gemfile.lock
+++ /dev/null
@@ -1,106 +0,0 @@
-GEM
-  remote: https://rubygems.org/
-  specs:
-    addressable (2.8.7)
-      public_suffix (>= 2.0.2, < 7.0)
-    bigdecimal (3.1.8)
-    colorator (1.1.0)
-    concurrent-ruby (1.3.4)
-    diff-lcs (1.5.1)
-    em-websocket (0.5.3)
-      eventmachine (>= 0.12.9)
-      http_parser.rb (~> 0)
-    eventmachine (1.2.7)
-    ffi (1.17.0-arm64-darwin)
-    forwardable-extended (2.6.0)
-    google-protobuf (4.28.0-arm64-darwin)
-      bigdecimal
-      rake (>= 13)
-    http_parser.rb (0.8.0)
-    i18n (1.14.5)
-      concurrent-ruby (~> 1.0)
-    jekyll (4.3.3)
-      addressable (~> 2.4)
-      colorator (~> 1.0)
-      em-websocket (~> 0.5)
-      i18n (~> 1.0)
-      jekyll-sass-converter (>= 2.0, < 4.0)
-      jekyll-watch (~> 2.0)
-      kramdown (~> 2.3, >= 2.3.1)
-      kramdown-parser-gfm (~> 1.0)
-      liquid (~> 4.0)
-      mercenary (>= 0.3.6, < 0.5)
-      pathutil (~> 0.9)
-      rouge (>= 3.0, < 5.0)
-      safe_yaml (~> 1.0)
-      terminal-table (>= 1.8, < 4.0)
-      webrick (~> 1.7)
-    jekyll-remote-theme (0.4.3)
-      addressable (~> 2.0)
-      jekyll (>= 3.5, < 5.0)
-      jekyll-sass-converter (>= 1.0, <= 3.0.0, != 2.0.0)
-      rubyzip (>= 1.3.0, < 3.0)
-    jekyll-sass-converter (3.0.0)
-      sass-embedded (~> 1.54)
-    jekyll-seo-tag (2.8.0)
-      jekyll (>= 3.8, < 5.0)
-    jekyll-watch (2.2.1)
-      listen (~> 3.0)
-    kramdown (2.4.0)
-      rexml
-    kramdown-parser-gfm (1.1.0)
-      kramdown (~> 2.0)
-    liquid (4.0.4)
-    listen (3.9.0)
-      rb-fsevent (~> 0.10, >= 0.10.3)
-      rb-inotify (~> 0.9, >= 0.9.10)
-    mercenary (0.4.0)
-    nokogiri (1.16.7-arm64-darwin)
-      racc (~> 1.4)
-    pathutil (0.16.2)
-      forwardable-extended (~> 2.6)
-    public_suffix (6.0.1)
-    racc (1.8.1)
-    rack (2.2.9)
-    rake (13.2.1)
-    rb-fsevent (0.11.2)
-    rb-inotify (0.11.1)
-      ffi (~> 1.0)
-    rexml (3.3.7)
-    rouge (4.3.0)
-    rspec (3.13.0)
-      rspec-core (~> 3.13.0)
-      rspec-expectations (~> 3.13.0)
-      rspec-mocks (~> 3.13.0)
-    rspec-core (3.13.1)
-      rspec-support (~> 3.13.0)
-    rspec-expectations (3.13.3)
-      diff-lcs (>= 1.2.0, < 2.0)
-      rspec-support (~> 3.13.0)
-    rspec-mocks (3.13.1)
-      diff-lcs (>= 1.2.0, < 2.0)
-      rspec-support (~> 3.13.0)
-    rspec-support (3.13.1)
-    rubyzip (2.3.2)
-    safe_yaml (1.0.5)
-    sass-embedded (1.78.0-arm64-darwin)
-      google-protobuf (~> 4.27)
-    terminal-table (3.0.2)
-      unicode-display_width (>= 1.1.1, < 3)
-    unicode-display_width (2.5.0)
-    webrick (1.8.1)
-
-PLATFORMS
-  arm64-darwin-23
-
-DEPENDENCIES
-  jekyll (~> 4.0)
-  jekyll-remote-theme
-  jekyll-seo-tag
-  nokogiri
-  rack (~> 2.2.4)
-  rspec
-  webrick (~> 1.8)
-
-BUNDLED WITH
-   2.3.26
diff --git a/_layouts/default.html b/_layouts/default.html
index c541e61..48d549b 100644
--- a/_layouts/default.html
+++ b/_layouts/default.html
@@ -1,6 +1,7 @@
 <!DOCTYPE html>
 <html lang="{{ site.lang | default: 'en-US' }}">
 <head>
+<base href="/bridgeAI-MLOps-knowledge-hub/">
 <meta charset="UTF-8">
 
 {% seo %}
@@ -16,7 +17,7 @@
 <a id="skip-to-content" href="#content">Skip to the content.</a>
 
 <header class="page-header" role="banner">
-    <a href="../"><img src="../assets/logo.png" class="top-left" width="124px" height="124px"></a>
+    <a href="./"><img src="./assets/logo.png" class="top-left" width="124px" height="124px"></a>
 <h1 class="project-name">{{ page.title | default: site.title | default: site.github.repository_name }}</h1>
 <br>
 <br>
@@ -263,30 +264,30 @@ <h1 class="project-name">{{ page.title | default: site.title | default: site.git
     color: var(--text);
     padding: 1rem;
     }
-
+    
     </style>
 
     <div id="mySidenav" class="sidenav">
-        <a href="../">Home</a>
+        <a href="./">Home</a>
         <br>
-        <a onclick="toggleNavMlops()"><img src="https://cdn0.iconfinder.com/data/icons/rounded-basics/24/rounded__menu-512.png" width="15px" height="15px"> MLOps: The Big Picture</a>
+        <a onclick="toggleNavMlops()"><img src="https://e7.pngegg.com/pngimages/217/548/png-clipart-drop-down-list-computer-icons-arrow-menu-hamburger-button-arrow-angle-rectangle.png" width="15px" height="15px"> MLOps: The Big Picture</a>
         <div id="myDropdownMlops" class="dropdown-mlops">
-            <a href="../mlops_big_picture/mlops_summary.html">Summary</a>
-            <a href="../mlops_big_picture/requirements_research.html">Requirements and Research</a>
-            <a href="../mlops_big_picture/versioning.html">Data Versioning</a>
-            <a href="../mlops_big_picture/DAG.html">Training Pipeline</a>
-            <a href="../mlops_big_picture/serving.html">Model Serving</a>
-            <a href="../mlops_big_picture/pred_service.html">Prediction Service</a>
-            <a href="../mlops_big_picture/monitoring.html">Model Monitoring</a>
-            <a href="../mlops_big_picture/gitops.html">GitOps</a>
-            <a href="../mlops_big_picture/team_arch.html">Team Architecture</a>
+            <a href="./mlops_big_picture/mlops_summary.html">Summary</a>
+            <a href="./mlops_big_picture/requirements_research.html">Requirements and Research</a>
+            <a href="./mlops_big_picture/versioning.html">Data Versioning</a>
+            <a href="./mlops_big_picture/DAG.html">Training Pipeline</a>
+            <a href="./mlops_big_picture/serving.html">Model Serving</a>
+            <a href="./mlops_big_picture/pred_service.html">Prediction Service</a>
+            <a href="./mlops_big_picture/monitoring.html">Model Monitoring</a>
+            <a href="./mlops_big_picture/gitops.html">GitOps</a>
+            <a href="./mlops_big_picture/team_arch.html">Team Architecture</a>
         </div>
-        <a onclick="toggleNavBusiness()"><img src="https://cdn0.iconfinder.com/data/icons/rounded-basics/24/rounded__menu-512.png" width="15px" height="15px"> Corporate Perspective</a>
+        <a onclick="toggleNavBusiness()"><img src="https://e7.pngegg.com/pngimages/217/548/png-clipart-drop-down-list-computer-icons-arrow-menu-hamburger-button-arrow-angle-rectangle.png" width="15px" height="15px"> Corporate Perspective</a>
         <div id="myDropdownBusiness" class="dropdown-business">
-            <a href="../corporate_perspective/deployment_lifecycle.html">Deployment Service Life Cycle</a>
-            <a href="../corporate_perspective/prerequisites.html">Skills, Roles and Tool Horizon Scan</a>
-            <a href="../corporate_perspective/maturity_assessment.html">Maturity Assessment</a>
-            <a href="../corporate_perspective/best_practices.html">MLOps Best Practices</a>
+            <a href="./corporate_perspective/deployment_lifecycle.html">Deployment Service Life Cycle</a>
+            <a href="./corporate_perspective/prerequisites.html">Skills, Roles and Tool Horizon Scan</a>
+            <a href="./corporate_perspective/maturity_assessment.html">Maturity Assessment</a>
+            <a href="./corporate_perspective/best_practices.html">MLOps Best Practices</a>
         </div>
 
 </div>
@@ -670,13 +671,13 @@ <h1 class="project-name">{{ page.title | default: site.title | default: site.git
 
     function toggleNavBusiness() {
     const dropdownbusiness = document.getElementById("myDropdownBusiness");
-    if (dropdownbusiness.style.height === "280px") {
+    if (dropdownbusiness.style.height === "300px") {
         dropdownbusiness.style.height = "0px";
         dropdownbusiness.style.opacity = "0";
         dropdownbusiness.style.visibility = "hidden";
         localStorage.setItem("dropdownBusinessState", "closed");
     } else {
-        dropdownbusiness.style.height = "280px";
+        dropdownbusiness.style.height = "300px";
         dropdownbusiness.style.opacity = "100";
         dropdownbusiness.style.visibility = "visible";
         localStorage.setItem("dropdownBusinessState", "open");
@@ -688,7 +689,7 @@ <h1 class="project-name">{{ page.title | default: site.title | default: site.git
         const dropdownState = localStorage.getItem("dropdownBusinessState");
         
         if (dropdownState === "open") {
-            dropdownbusiness.style.height = "280px";
+            dropdownbusiness.style.height = "300px";
             dropdownbusiness.style.opacity = "100";
             dropdownbusiness.style.visibility = "visible";
         } else {
diff --git a/corporate_perspective/best_practices.md b/corporate_perspective/best_practices.md
index d19b776..b272f00 100644
--- a/corporate_perspective/best_practices.md
+++ b/corporate_perspective/best_practices.md
@@ -10,7 +10,7 @@ title: BridgeAI MLOps Knowledge Hub
 <span class="callout-icon">ℹ️</span>
     <br>
     <br>
-    While best practices for MLOps is technically covered in the <a href="https://digicatapult.github.io/bridgeAI-MLOps-knowledge-hub/prerequisites.html" target="_blank">Prerequisites</a> section, separate elaboration on these principles is required given their importance.
+    While best practices for MLOps is technically covered in the <a href="./corporate_perspective/prerequisites.html" target="_blank">Prerequisites</a> section, separate elaboration on these principles is required given their importance.
 </blockquote>
 <br>
 
@@ -38,7 +38,7 @@ because tools for implementation may be costly, BUT the tools used to create the
 
 When developing your metrics, it is important to ensure that the process put in place to meet your business goal is reviewed thoroughly and regularly, as automation will address areas in which the current process faces challenges.
 
-The [Deployment Service Life Cycle framework](https://digicatapult.github.io/bridgeAI-MLOps-knowledge-hub/deployment_lifecycle.html){:target="_blank"} provided in this hub contains a table of considerations to adequately clarify your business objectives, resource constraints (funding, time, in/tangible resources), and AI/ML use cases.
+The [Deployment Service Life Cycle framework](./corporate_perspective/deployment_lifecycle.html){:target="_blank"} provided in this hub contains a table of considerations to adequately clarify your business objectives, resource constraints (funding, time, in/tangible resources), and AI/ML use cases.
 
 
 ### Infrastructure Best Practices
@@ -47,7 +47,7 @@ The right infrastructure must be in place to support the model <span style="colo
 
 Key best practices when designing the infrastructure include selecting the <span style="color:#8C1437">right infrastructure components</span> that align with your scope/requirements/constraints, deciding between <span style="color:#8C1437">cloud-based and on-premise</span> infrastructure, and ensuring that the <span style="color:#8C1437">infrastructure is scalable</span>. 
 
-The right components can be derived from a range of containers, orchestration tools, software environments, CI/CD tools; these must be implemented step-wise regarding the flow of your ML pipeline. This hub offers a [Horizon Scan](https://digicatapult.github.io/bridgeAI-MLOps-knowledge-hub/prerequisites.html#gitops){:target="_blank"} to assist you with identifying the ideal tools for your infrastructure as it relates to [GitOps](https://about.gitlab.com/topics/gitops/){:target="_blank"}.
+The right components can be derived from a range of containers, orchestration tools, software environments, CI/CD tools; these must be implemented step-wise regarding the flow of your ML pipeline. This hub offers a [Horizon Scan](./corporate_perspective/prerequisites.html#gitops){:target="_blank"} to assist you with identifying the ideal tools for your infrastructure as it relates to [GitOps](https://about.gitlab.com/topics/gitops/){:target="_blank"}.
 
 When deciding between cloud-based and on-premise infrastructure, three main points organisations should consider <span style="color:#8C1437">alongside</span> their scope, requirements and constraints are whether their choice of infrastructure is:
 
@@ -85,7 +85,7 @@ The development and documentation of your model's training metrics can be execut
 
 ### Code Best Practices
 
-The code that is written must execute effectively at all stages of your pipeline. All relevant actors in your MLOps team (<span style="color:#8C1437">examples of actors</span> can be found in the [Skills, Roles and Tool Horizon Scan](https://digicatapult.github.io/bridgeAI-MLOps-knowledge-hub/prerequisites.html#roles){:target="_blank"} page of this hub) must be able to read, write or execute model codes. 
+The code that is written must execute effectively at all stages of your pipeline. All relevant actors in your MLOps team (<span style="color:#8C1437">examples of actors</span> can be found in the [Skills, Roles and Tool Horizon Scan](./corporate_perspective/prerequisites.html#roles){:target="_blank"} page of this hub) must be able to read, write or execute model codes. 
 
 Where unit tests will evaluate individual features, continuous integration implementations will test the pipeline as a whole to guarantee that changes in the code will not break the model.
 
diff --git a/index.md b/index.md
index 9eca636..b54766e 100644
--- a/index.md
+++ b/index.md
@@ -16,7 +16,7 @@ As one of the four main strategic partners of BridgeAI, Digital Catapult plays a
 
 Organisations often face numerous organisational, technical and operational challenges when transitioning their ML (Machine learning) models from development to production. These challenges include complexity in integration, lack of required skills and expertise, absence of mature tools and robust frameworks for ML Operations (MLOps) and more. In response to these challenges, Digital Catapult has developed its Applied AI suite offering, which includes the following:
 
-* A comprehensive, web accessible [MLOps maturity assessment](https://digicatapult.github.io/bridgeAI-MLOps-knowledge-hub/maturity_assessment.html){:target="_blank"}.
+* A comprehensive, web accessible [MLOps maturity assessment](./corporate_perspective/maturity_assessment.html){:target="_blank"}.
 * An end-to-end pre-built MLOps pipeline, created using open-source tools.
     * This pipeline has been designed to give SMEs the opportunity to engage with practical tools and resources that can notably enhance their automated AI/ML offerings, which is expanded on in the MLOps Clinic.
     * It has also been designed to give SMEs the opportunity to speed up the development of their AI/ML offerings that they seek to automate and deploy, with minimal coding on their behalf.
@@ -50,4 +50,4 @@ This knowledge hub has been designed to explain the design and implementation of
 
 This knowledge hub does not provide any <span style="color:#8C1437">extensive</span> information on what MLOps is, its best practices, or on the functions of individual components of an MLOps pipeline. It does, however, provide links to platforms that address these points.
 
-The links to these platforms can be found in the [Horizon Scan](https://digicatapult.github.io/bridgeAI-MLOps-knowledge-hub/prerequisites.html#design-decisions){:target="_blank"} page, and the [Best Practices](https://digicatapult.github.io/bridgeAI-MLOps-knowledge-hub/best_practices.html#resources){:target="_blank"} page of this hub.
\ No newline at end of file
+The links to these platforms can be found in the [Horizon Scan](./corporate_perspective/prerequisites.html#design-decisions){:target="_blank"} page, and the [Best Practices](./corporate_perspective/best_practices.html#resources){:target="_blank"} page of this hub.
\ No newline at end of file
diff --git a/mlops_big_picture/mlops_summary.md b/mlops_big_picture/mlops_summary.md
index c742967..1ec740a 100644
--- a/mlops_big_picture/mlops_summary.md
+++ b/mlops_big_picture/mlops_summary.md
@@ -12,13 +12,14 @@ MLOps is a set of practices that aims to unify the release cycle for machine lea
 
 Each page under "MLOps: The Big Picture" has been designed to address the research, implementation and troubleshooting regarding each component of the team's MLOps pipeline. The components, which are reflective of the typical order in which data flows through an MLOps pipeline, comprise:
 
-<img src="../assets/pipeline.png" width="619.5" height="619.5" alt="MLOps Pipeline Data FLow" class="center"/>
+<img src="./assets/pipeline.png" width="619.5" height="619.5" alt="MLOps Pipeline Data FLow" class="center"/>
+
 <!-- this image is to be replaced with flow diagram, to better show the data that flows through each stage -->
 
 
 <span style="color:#8C1437">NOTE:</span> While GitOps is not a component of an MLOps pipeline that data can flow through, it is the foundation on which the DC AI/ML team built the MLOps pipeline available to you and is therefore discussed as part of the architecture of the pipeline.
 
-Overviews of each component through which data flows in an MLOps pipeline can be found in the [Horizon Scan](https://digicatapult.github.io/bridgeAI-MLOps-knowledge-hub/prerequisites.html#architecture-overview){:target="_blank"} page.
+Overviews of each component through which data flows in an MLOps pipeline can be found in the [Horizon Scan](./corporate_perspective/prerequisites.html#architecture-overview){:target="_blank"} page.
 
 ## Corporate Perspective Summary
 
@@ -26,7 +27,7 @@ Aside from the research and implementation surrounding each component of our pip
 
 <span style="color:#8C1437"><b>Corporate perspective</b></span> considerations include a maturity assessment for evaluating the viability of your MLOps pipeline, required skills and roles for automating your AI/ML offering, and a horizon scan of tools you can use for each component of the pipeline. 
 
-A [Deployment Service Life Cycle framework](https://digicatapult.github.io/bridgeAI-MLOps-knowledge-hub/deployment_lifecycle.html){:target="_blank"} is also available for you to use as a guide for additional considerations in this context. This framework serves as a starting point for ensuring that organisations adequately gather information on their requirements (as well as use cases, current infrastructure requirements, and constraints) before assessing these and designing and implementing an AI/ML offering around them. It also acts as a reminder for points of review for the offering after it has been implemented. 
+A [Deployment Service Life Cycle framework](./corporate_perspective/deployment_lifecycle.html){:target="_blank"} is also available for you to use as a guide for additional considerations in this context. This framework serves as a starting point for ensuring that organisations adequately gather information on their requirements (as well as use cases, current infrastructure requirements, and constraints) before assessing these and designing and implementing an AI/ML offering around them. It also acts as a reminder for points of review for the offering after it has been implemented. 
 
 ## Resources
 
diff --git a/mlops_big_picture/requirements_research.md b/mlops_big_picture/requirements_research.md
index 45c608d..a9df50b 100644
--- a/mlops_big_picture/requirements_research.md
+++ b/mlops_big_picture/requirements_research.md
@@ -3,6 +3,15 @@ layout: default
 title: BridgeAI MLOps Knowledge Hub
 ---
 
+## Where did the journey start? How were the design decisions made across the pipeline for each component?
+
+<!-- Our journey towards the creation of an end-to-end MLOps pipeline began as an extension of our AI Adoption Assessment initiative completed in collaboration with BridgeAI -->
+
+We decided on different tools for different components of our pipeline such as registry, data versioning and model monitoring by conducting spikes (research) for optimal tools based on our requirements. This research is covered in the pages underneath <span style="color:#8C1437"><b>"MLOps: The Big Picture"</b></span>.
+
+Some components did not have formal research conducted, and were instead decided on because they are widely used in the industry and therefore have in-depth documentation/community notes.
+
+
 ## What were the requirements for our ML model and the MLOps pipeline?
 
 We wanted to demonstrate an end-to-end, open source, pre-made MLOps pipeline. We opted for keeping our execution of this simple by creating a deploy-model pipeline only, rather than creating variations of the pipeline (eg deploy-code), which we would share with startups as a starting point.
@@ -12,10 +21,3 @@ Our base requirements:
 <!-- 2. The level of MLOps maturity we aimed for in the ML maturity assessment and why -->
 3. We should have a Minimum Viable MLOps pipeline with basic automation
 
-
-## Where did the journey start? How were the design decisions made across the pipeline for each component?
-
-We decided on different tools for different components of our pipeline such as registry, data versioning and model monitoring by conducting spikes (research) for optimal tools based on our requirements. This research is covered in the pages underneath <span style="color:#8C1437"><b>"MLOps: The Big Picture"</b></span>.
-
-Some components did not have formal research conducted, and were instead decided on because they are widely used in the industry and therefore have in-depth documentation/community notes.
-
diff --git a/mlops_big_picture/serving.md b/mlops_big_picture/serving.md
index 561b46a..4ec1eed 100644
--- a/mlops_big_picture/serving.md
+++ b/mlops_big_picture/serving.md
@@ -482,13 +482,13 @@ Recommended | Yes | Can be considered | Yes
 
 ## Questions:
 
-Q. Decision between KServe or Seldon core - Which one is more suitable for our use case?
-    <br>
-    <br>
-    A comparison - [KServe vs. Seldon Core - Superwise ML Observability](https://superwise.ai/blog/kserve-vs-seldon-core/){:target="_blank"}
-    <br>
-    <br>
-    KServe is open source whereas [SeldonCore](https://www.seldon.io/pricing){:target="_blank"} is expensive. And so is [BentoCloud](https://www.bentoml.com/pricing){:target="_blank"}.
+1 - Decision between KServe or Seldon core - Which one is more suitable for our use case?
+<br>
+
+  - A comparison - [KServe vs. Seldon Core - Superwise ML Observability](https://superwise.ai/blog/kserve-vs-seldon-core/){:target="_blank"}
+  <br>
+
+  - KServe is open source whereas [SeldonCore](https://www.seldon.io/pricing){:target="_blank"} is expensive. And so is [BentoCloud](https://www.bentoml.com/pricing){:target="_blank"}.
 
 ## Resources
 
diff --git a/mlops_big_picture/team_arch.md b/mlops_big_picture/team_arch.md
index e09eceb..ab5fcc0 100644
--- a/mlops_big_picture/team_arch.md
+++ b/mlops_big_picture/team_arch.md
@@ -9,7 +9,8 @@ title: BridgeAI MLOps Knowledge Hub
 
 Click on a number icon in the image for a brief description of the process associated with it.
 
-<img src="../assets/mlops_architecture.png" usemap = "#arch" width="839" height="650.64"/>
+<img src="./assets/mlops_architecture.png" usemap = "#arch" width="839" height="650.64"/>
+
 <!-- intrinsic size - 1980 by 1542; div w and h by 2.36 -->
 
 <map name = "arch">
diff --git a/mlops_big_picture/versioning.md b/mlops_big_picture/versioning.md
index 47f554e..785e8a5 100644
--- a/mlops_big_picture/versioning.md
+++ b/mlops_big_picture/versioning.md
@@ -38,7 +38,8 @@ title: BridgeAI MLOps Knowledge Hub
 </blockquote>
 
 <img src="../assets/image-20240724-204151.png">
-An image showing the different versions of data, features and model. Source: [DVC](https://dvc.org/doc/use-cases/versioning-data-and-model-files)
+An image showing the different versions of data, features and model. Source: [DVC](https://dvc.org/doc/use-cases/versioning-data-and-model-files){:target="_blank"}
+
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