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* 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 <[email protected]>
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joannesmadu and Joannes Madu authored Oct 11, 2024
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3 changes: 2 additions & 1 deletion .gitignore
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*/.DS_Store
.DS_Store
_site/
_site/*
_site/*
Gemfile.lock
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43 changes: 22 additions & 21 deletions _layouts/default.html
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<!DOCTYPE html>
<html lang="{{ site.lang | default: 'en-US' }}">
<head>
<base href="/bridgeAI-MLOps-knowledge-hub/">
<meta charset="UTF-8">

{% seo %}
Expand All @@ -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>
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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>
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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");
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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 {
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8 changes: 4 additions & 4 deletions corporate_perspective/best_practices.md
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Expand Up @@ -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>

Expand Down Expand Up @@ -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
Expand All @@ -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:

Expand Down Expand Up @@ -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.

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Expand Up @@ -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.
Expand Down Expand Up @@ -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.
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.
7 changes: 4 additions & 3 deletions mlops_big_picture/mlops_summary.md
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Expand Up @@ -12,21 +12,22 @@ 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

Aside from the research and implementation surrounding each component of our pipeline, we also captured aspects of our corporate approaches to this project for your consideration.

<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

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