Machine Learning (ML) Platform & Ops Team

An Machine Learning (ML) Platform & Ops Team is a internal platform team/devops team for an in-house ML platform system.

• Context:
  • It can manage and maintain the Machine Learning Infrastructure for smooth operation and deployment.
  • It can monitor and optimize the performance of Machine Learning Models in production.
  • It can automate the Machine Learning Pipeline to ensure seamless integration of data, training, and deployment.
  • It can be composed of:
    • ML Platform Engineers.
    • ML Platform Product Manager.
  • ...
• Example(s):
  • Netflix's ML Platform Team.
  • Uber's ML Platform Team.
  • AirBnB's ML Platform Team.
  • PlayStations's ML Platform Team.
  • ...
• Counter-Example(s):
  • a DevOps Team.
• See: AI Platform Team, ML Engineering Team.

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References

2023

• chat
  • An ML Platform Ops Team is a specialized team that focuses on the operational aspects of Machine Learning platforms and infrastructure.
  • Also known as: MLOps Team, Machine Learning Operations Team, ML Platform Operations Team.
  • It can manage and maintain the Machine Learning Infrastructure for smooth operation and deployment.
  • It can monitor and optimize the performance of Machine Learning Models in production.
  • It can automate the Machine Learning Pipeline to ensure seamless integration of data, training, and deployment.
  • Associated concepts: MLOps, Data Engineering, Data Science, Model Deployment, Model Monitoring

2023

• https://jobs.netflix.com/jobs/278437235
  • We are creating a new Product Management role to increase the leverage of our Machine Learning Platform.
  • In this role, you will:
    • Define the strategic vision for MLP, objectives, key results and success metrics in partnership with Engineering and in alignment with broader business objectives.
    • Collect feedback and understand user needs from ML/AI practitioners and application engineers across Netflix, deriving product requirements and sizing their importance to then prioritize areas of investment.
    • Ensure product success throughout the entire lifecycle of strategically important initiatives, from ideation to full launch and ongoing support.
    • Socialize strategic plans, roadmaps, and ongoing progress to stakeholders and executives in product review forums, quarterly OKR check-ins, etc.
    • Create programs around user education and support for users of the ML Platform.
    • Follow and assess external industry trends and innovations.

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