Machine Learning (ML)-based System Development Team

An Machine Learning (ML)-based System Development Team is a AI system software development team that focuses on designing, building, and deploying ML-based systems.

• AKA: Machine Learning Engineering Team, ML Development Team, ML System Team.
• Context:
  • It can (typically) implement ML Pipelines through automated processes.
  • It can (typically) maintain Model Quality through validation checks.
  • It can (often) leverage ML Platform Systems through infrastructure services.
  • It can (often) design ML Architecture through system planning.
  • It can (often) work with Data Scientists for model refinement.
  • It can (often) collaborate with ML Ops Teams for system reliability.
  • It can (often) partner with Data Engineers for data pipelines.
  • It can (often) coordinate with Software Engineers for system integration.
  • ...
  • It can range from being a Research Oriented ML Team to being a Production Focused ML Team, depending on its development focus.
  • It can range from being a Specialized ML Team to being a General ML Team, depending on its application scope.
  • It can range from being a Feature Development Team to being a Platform Development Team, depending on its technical focus.
  • It can range from being a Homogeneous ML Team to being a Heterogeneous ML Team, depending on its skill diversity.
  • It can range from being a Small ML Team to being a Large ML Team, depending on its organizational scale.
  • It can range from being a Centralized ML Team to being a Decentralized ML Team, depending on its organizational structure.
  • ...
  • It can conduct Feature Engineering through data transformation.
  • It can optimize ML Algorithms through performance tuning.
  • It can ensure Model Compatibility through system adaptation.
  • It can manage Technical Debt through code maintenance.
  • ...
  • It can establish ML Development Team Culture that values:
    • Code Quality through complexity reduction.
    • System Stability through reliability improvement.
    • Model Reproducibility through process standardization.
    • Feature Cleanup through unnecessary removal.
  • ...
• Examples:
  • Production ML Teams, such as:
    • Recommendation System Teams, such as:
      • Content Recommendation Team for personalized suggestions.
      • Product Recommendation Team for purchase predictions.
    • Computer Vision Teams, such as:
      • Image Recognition Team for visual analysis.
      • Video Processing Team for motion analysis.
  • Research ML Teams, such as:
    • Algorithm Development Teams, such as:
      • Model Architecture Team for neural network research.
      • Optimization Team for learning process improvement.
    • Application Research Teams, such as:
      • Domain Adaptation Team for transfer learning research.
      • Feature Learning Team for representation studies.
  • Infrastructure ML Teams, such as:
    • Platform Development Teams, such as:
      • Training Infrastructure Team for model training systems.
      • Serving Infrastructure Team for model serving platforms.
    • MLOps Teams, such as:
      • Model Monitoring Team for production reliability.
      • Pipeline Infrastructure Team for automated workflows.
  • Domain ML Teams, such as:
    • Natural Language Teams, such as:
      • Text Processing Team for language understanding.
      • Speech Recognition Team for audio processing.
    • Time Series Teams, such as:
      • Forecasting Team for predictive modeling.
      • Anomaly Detection Team for pattern analysis.
  • ...
• Counter-Examples:
  • Data Science Team, which focuses on exploratory analysis rather than production systems.
  • Software Development Team, which builds general applications rather than ML systems.
  • Research Team, which conducts basic research rather than applied development.
  • Platform Operations Team, which maintains infrastructure rather than developing models.
• See: ML Development Process, ML-based Project, ML Engineering Team Culture, Software Engineering Team.

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References

2025-01-17

• Perplexity.ai
  • Question: What is a Machine Learning System Development Team structure? Explain its core composition, organization models, and development practices. Include information about technical and leadership roles, different team structures, and best practices. Also describe the challenges and their solutions.
  • Answer: A Machine Learning System Development Team is a specialized group that combines AI expertise with software engineering practices to build production-ready ML systems.
    • Core Team Composition:
      • Technical Roles:
        • ML Engineers deploy and maintain ML models in production.
        • Data Scientists develop and validate ML models.
        • Data Engineers manage data pipelines and infrastructure.
        • Software Engineers handle system integration and deployment.
      • Leadership Roles:
        • Technical Lead/ML Architect guides technical decisions and system design.
        • Project Manager coordinates team efforts and manages deliverables.
        • Product Manager aligns ML solutions with business objectives.
    • Team Organization Models:
      • By Development Focus:
        • Production-focused Teams emphasize model deployment and system reliability.
        • Research-oriented Teams focus on model innovation and algorithm development.
        • Platform Teams build and maintain ML infrastructure.
      • By Structure:
        • Centralized: Single team handling all ML initiatives.
        • Decentralized: Specialized teams for specific ML products.
        • Hybrid: Combined approach with shared resources.
    • Development Practices:
      • ML Pipeline Management:
        • Automated training and deployment processes.
        • Continuous model monitoring and validation.
        • Version control for data, models, and code.
      • Quality Assurance:
        • Model validation and testing procedures.
        • Performance monitoring and optimization.
        • Reproducibility checks and validation.
  • Citations:

[1] https://mindtitan.com/resources/blog/how-to-put-together-a-machine-learning-team-for-an-ai-project/
[2] https://neptune.ai/blog/ml-collaboration-best-practices-from-ml-teams
[3] https://se-ml.github.io/practices/
[4] https://neptune.ai/blog/roles-in-ml-team-and-how-they-collaborate
[5] https://www.snowflake.com/trending/machine-learning-pipeline-ml-lifecycle/
[6] https://attri.ai/blog/best-practices-for-ml-teams

2023

• chat
  • An ML Engineering Team is a specialized software product engineering team that focuses on designing, building, and deploying Machine Learning Models to create innovative solutions or enhance existing products and services.
  • Also known as: Machine Learning Engineering Team, ML Development Team.
  • It can leverage the ML Platform system managed by the ML Ops Team to streamline the process of model development, training, and deployment.
  • It can work closely with Data Scientists to refine models, conduct feature engineering, and optimize algorithms for real-world applications.
  • It can integrate machine learning models into software products or services, ensuring their compatibility with existing systems and infrastructure.
  • It can collaborate with other technical teams, such as data engineers and software engineers, to build end-to-end machine learning solutions that meet the requirements and constraints of specific use cases.
  • It can monitor the performance of deployed models, iterating on them when necessary, and working with the ML Ops Team to maintain the overall health and reliability of the machine learning system.
  • Associated concepts: Data Science, Data Engineering, Model Deployment, Model Monitoring, ML Platform Ops Team

2015

• (Sculley et al., 2015) ⇒ D. Sculley, Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, Michael Young, Jean-Francois Crespo, and Dan Dennison. (2015). “Hidden Technical Debt in Machine Learning Systems.” In: Proceedings of the 28th International Conference on Neural Information Processing Systems (NIPS 28).
  • QUOTE: ... Cultural Debt. There is sometimes a hard line between ML research and engineering, but this can be counter-productive for long-term system health. It is important to create team cultures that reward deletion of features, reduction of complexity, improvements in reproducibility, stability, and monitoring to the same degree that improvements in accuracy are valued. In our experience, this is most likely to occur within heterogeneous teams with strengths in both ML research and engineering. ...

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