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Joannes Madu committed Oct 10, 2024
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28 changes: 14 additions & 14 deletions _layouts/default.html
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Expand Up @@ -267,26 +267,26 @@ <h1 class="project-name">{{ page.title | default: site.title | default: site.git
</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>
<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>
<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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4 changes: 2 additions & 2 deletions index.md
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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](../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](../prerequisites.html#design-decisions){:target="_blank"} page, and the [Best Practices](../best_practices.html#resources){:target="_blank"} page of this hub.
4 changes: 2 additions & 2 deletions mlops_big_picture/mlops_summary.md
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Expand Up @@ -18,15 +18,15 @@ Each page under "MLOps: The Big Picture" has been designed to address the resear

<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](../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](../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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16 changes: 9 additions & 7 deletions mlops_big_picture/requirements_research.md
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Expand Up @@ -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.
Expand All @@ -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.

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