OER·harvester

Competency catalogue

The 104 active T2.3 competencies: the vocabulary the harvester searches with, the descriptor the alignment reads, and what the alignment found. Compose a harvest →

What the alignment foundHow often each was searched

How many resources the alignment reads as contributing to each competency or developing it: decided at level 2 or 3, or not decided yet and more likely than not to reach 2. 54 competencies have none, 24 one or two. Every figure is a lower bound: on a sample graded on every competency, the map counts 33% of what was graded 2 or 3, and almost nothing of the 5 competencies underlined with dots.

How many times a harvest has searched for each competency, once for each source it asked. 101 competencies have never been searched. A search is not material found: the aligned view says what came of it.

none 1–2 3–9 10–29 30+
never 1–2 3–5 6–11 12+
DDigital 94212203420012149086012303134 GGreen 0000000102000010000100010 RResilience & Entrepreneurial 004000000000000010000120000 TDeep Tech 72643202372825212335201140010

T14 Deep Tech · Skill · Advanced

MLOps & Lifecycle Management

Apply DevOps principles to ML lifecycle

Alignment

1 resource likely to contribute to it or develop it

0 decided at level 2 or 3 · 1 not decided yet, more likely than not

What the map misses here is not measured: the graded sample holds no pair graded 2 or 3 for it.

Retrieval

0 resources harvested by searching its terms

Never searched yet.

Search vocabulary

Domain group
MLOpsmachine learning lifecyclemodel registrycontinuous trainingML pipelinemodel versioningML monitoringDevOps for machine learning

Zenodo and arXiv take the whole group in one query. GitHub takes one phrase at a time and works left to right, stopping once the competency is satisfied — so on that lane this order decides which terms actually run.

The compiled queries
zenodo ("artificial intelligence" OR "machine learning" OR "generative AI" OR "deep learning" OR "reinforcement learning" OR "large language model" OR "AI") AND ("MLOps" OR "machine learning lifecycle" OR "model registry" OR "continuous training" OR "ML pipeline" OR "model versioning" OR "ML monitoring" OR "DevOps for machine learning")
arxiv (all:"artificial intelligence" OR all:"machine learning" OR all:"generative AI" OR all:"deep learning" OR all:"reinforcement learning" OR all:"large language model") AND (all:"MLOps" OR all:"machine learning lifecycle" OR all:"model registry" OR all:"continuous training" OR all:"ML pipeline" OR all:"model versioning" OR all:"ML monitoring" OR all:"DevOps for machine learning")
github · term 1 AI "MLOps" in:name,description,readme fork:false archived:false
github · term 2 AI "machine learning lifecycle" in:name,description,readme fork:false archived:false
github · term 3 AI "model registry" in:name,description,readme fork:false archived:false
github · term 4 AI "continuous training" in:name,description,readme fork:false archived:false
github · term 5 AI "ML pipeline" in:name,description,readme fork:false archived:false
github · term 6 AI "model versioning" in:name,description,readme fork:false archived:false
github · term 7 AI "ML monitoring" in:name,description,readme fork:false archived:false
github · term 8 AI "DevOps for machine learning" in:name,description,readme fork:false archived:false

Profile a129f609ea1a4992

Alignment descriptor draft

Outcomes
  • Can apply DevOps practices to machine learning: versioning, CI/CD, registries.
  • Can automate training and deployment pipelines.
  • Can track experiments and models across their lifecycle.
Develops it

Hands-on material on MLOps — pipelines, model registries, experiment tracking, continuous training — where the learner sets them up.

Only mentions it

A model saved to disk with no lifecycle management.

Alignment terms
mlopsmodel registrymlflowexperiment trackingml pipelinecontinuous trainingmodel versioning

Read in a resource's text beside the domain group. They are never compiled into a search.

Neighbours

Descriptor 9709c10e4f7d

How a vocabulary becomes a search

Every candidate must match both concept groups in its title, description or keywords before any content is downloaded. Precision warnings are recorded for audit; they never silently cancel positive evidence.

  1. Official name and description
  2. AI/ML terms and domain terms
  3. Zenodo, arXiv and GitHub syntax
  4. Metadata relevance gate
Mandatory AI/ML group
artificial intelligencemachine learninggenerative AIdeep learningreinforcement learninglarge language modelAI

GitHub's repository search takes one phrase at a time. Measured against the live endpoint, a parenthesised OR group returns nothing at all where each term alone returns thousands, and the 256-character limit rejects a long group outright — so that lane issues one query per term and stops as soon as the competency is satisfied.

coverage-vocabulary-1.3.0+sha256:08ad2cd19868 · catalogue sha256:04970bfc77255b1f

What a descriptor is

A descriptor is how the alignment reads a competency: what a learner who has it can do, what a resource has to do to develop it, what only mentions it, the phrases its subject is written in, its false friends, and how to tell it from the competencies that share its words. The definition above it stays the authority.

Every descriptor is a draft until a partner approves it; 0 of 104 are approved. Read the alignment rubric →

competency-descriptors-1.0.0+sha256:3cc741a61ced