Machine Learning Engineering (MLE) Task

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

• Context:
  • It can (often) be performed by a Machine Learning Engineer using an ML platform instance.
  • It can (often) be related to AI Engineering Task.
  • ...
  • 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):
  • to 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

2024

• Perplexity.ai
  • Core Responsibilities

- Designing and developing machine learning systems and algorithms[1][2] - Implementing ML models in production environments[4] - Building data pipelines to feed ML models[3] - Optimizing and improving existing ML models[2][4]

• • Key Activities

- Analyzing large datasets to determine the best methods for data preparation[2] - Selecting appropriate datasets and data representation methods[1] - Running machine learning tests and experiments[1][2] - Performing statistical analysis and fine-tuning models based on test results[1][2] - Extending existing ML libraries and frameworks as needed[1][3]

• • Production Focus

- Deploying models to production, often as APIs that can serve predictions to end users[2] - Scaling ML systems to handle large-scale data and user loads[2] - Monitoring and maintaining ML models in production environments[2][4] - Optimizing model performance and efficiency for real-world applications[2][4]

• • Collaboration

- Data scientists to implement their prototypes and models[1][2] - Software engineers to integrate ML systems into larger applications[5] - Product managers and stakeholders to understand business requirements[2]

• • Technical Skills

- Programming skills, especially in languages like Python, Java, and R[1][3] - Knowledge of ML frameworks like TensorFlow, PyTorch, and scikit-learn[3][4] - Expertise in data structures, algorithms, and software architecture[3] - Understanding of big data technologies like Hadoop and Spark[4]

• • Citations:

[1] https://resources.workable.com/machine-learning-engineer-job-description
[2] https://www.run.ai/guides/machine-learning-engineering
[3] https://business.linkedin.com/talent-solutions/resources/how-to-hire-guides/machine-learning-engineer/job-description
[4] https://www.simplilearn.com/machine-learning-engineer-job-description-article
[5] https://handbook.gitlab.com/job-families/engineering/development/data-science/machine-learning/
[6] https://www.coursera.org/articles/what-is-machine-learning-engineer
[7] https://www.reddit.com/r/MachineLearning/comments/17hwwf0/d_what_are_your_duties_as_a_machine_learning/
[8] https://www.indeed.com/hire/job-description/machine-learning-engineer

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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