Machine Learning (ML) Engineering Task

A Machine Learning (ML) Engineering Task is a software engineering task that creates ML-based production system.

• Context:
  • It (often) can be performed by a Machine Learning Engineer using an ML platform instance.
  • It can be a responsibility of an ML Engineering Job.
  • It can be supported by an ML Engineering System (possibly developed using an ML platform).
  • It can follow an ML Engineering Practice (such as ML engineering best-practices).
  • It can include:
    • ML Dataset Engineering Tasks.
    • ML Feature Management Tasks, such as ML Feature Generation.
    • ML Model Deployment Task.
    • ML Model Management Tasks.
    • ML Job Management Tasks.
  • …
• Example(s):
  • create a new Product-purchase recommendation service.
  • …
• Counter-Example(s):
  • Data Engineering Task.
  • Database Management Task.
  • …
• See: Data Engineering, Data Science Task.

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References

2021

• "Google Computing Cloud Professional Machine Learning Engineer Certification."
  • QUOTE: ... A Professional Machine Learning Engineer designs, builds, and productionizes ML models to solve business challenges using Google Cloud technologies and knowledge of proven ML models and techniques. The ML Engineer considers responsible AI throughout the ML development process, and collaborates closely with other job roles to ensure long-term success of models. The ML Engineer should be proficient in all aspects of model architecture, data pipeline interaction, and metrics interpretation. The ML Engineer needs familiarity with foundational concepts of application development, infrastructure management, data engineering, and data governance. Through an understanding of training, retraining, deploying, scheduling, monitoring, and improving models, the ML Engineer designs and creates scalable solutions for optimal performance.

    The Professional Machine Learning Engineer exam assesses your ability to:

    • Frame ML problems.
    • Architect ML solutions.
    • Design data preparation and processing systems.
    • Develop ML models.
    • Automate and orchestrate ML pipelines.
    • Monitor, optimize, and maintain ML solutions ...

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