diff --git a/_layouts/default.html b/_layouts/default.html index c541e61..b1b5d5f 100644 --- a/_layouts/default.html +++ b/_layouts/default.html @@ -267,26 +267,26 @@

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- Home + Home
MLOps: The Big Picture Corporate Perspective
diff --git a/index.md b/index.md index 9eca636..086b07f 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](../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 extensive 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](../prerequisites.html#design-decisions){:target="_blank"} page, and the [Best Practices](../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..fb090c0 100644 --- a/mlops_big_picture/mlops_summary.md +++ b/mlops_big_picture/mlops_summary.md @@ -18,7 +18,7 @@ Each page under "MLOps: The Big Picture" has been designed to address the resear NOTE: 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 @@ -26,7 +26,7 @@ Aside from the research and implementation surrounding each component of our pip Corporate perspective 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 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? + + + +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 "MLOps: The Big Picture". + +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: 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 "MLOps: The Big Picture". - -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. -