Machine Learning (ML) Model Deployment Task

A Machine Learning (ML) Model Deployment Task is a software deployment task that involves an ML model.

• Context:
  • It can (typically) be solved by an ML Model Deployment System.
  • It can range from being an Online ML Model Deployment Task to being an Offline ML Model Deployment Task.
  • It can involve a ML Model Offline Evaluation Task.
  • …
• Example(s):
  • a Predictive Function Deployment Task.
  • a Clustering Model Deployment Task.
  • …
• Counter-Example(s):
  • Data Processing Task.
  • Model Training Task.
  • ML Model Monitoring Task.
• See: ML Model Development, Software Development, API, Production Code.

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References

2018

• (Wei & Cai, 2019) ⇒ Kewei Wei, and Guanjun Cai (2018) "Bring Intelligence to Where Critical Transactions Run–An Update from Machine Learning for z/OS". Released: June 7, 2018 08:12 AM, Updated: June 7, 2018 08:12 AM
  • QUOTE: To support such a broadscale collaboration, your machine learning platform must have a well-defined machine learning workflow, from data ingestion and model training to model deployment, as shown below.

1998

• (Kohavi & Provost, 1998) ⇒ Ron Kohavi, and Foster Provost. (1998). “Glossary of Terms.” In: Machine Leanring 30(2-3).
  • Model deployment: The use of a learned model. Model deployment usually denotes applying the model to real data.

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