[D] 5 considerations for Deploying Machine Learning Models in Production – what did I miss?

• evidently

  4 2,359 9.8 Python

  Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b

  

  Consideration Number #5: For model observability look to Evidently.ai, Arize.ai, Arthur.ai, Fiddler.ai, Valohai.com, or whylabs.ai.

• MLflow

  20 11,867 9.8 Python

  Open source platform for the machine learning lifecycle

  

  Consideration Number #2: Consider using model life cycle development and management platforms like MLflow, DVC, Weights & Biases, or SageMaker Studio. And Ray, Ray Tune, Ray Train (formerly Ray SGD), PyTorch and TensorFlow for distributed, compute-intensive and deep learning ML workloads.

• Scout APM

  scoutapm.com

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  Less time debugging, more time building. Scout APM allows you to find and fix performance issues with no hassle. Now with error monitoring and external services monitoring, Scout is a developer's best friend when it comes to application development.

  

• dvc

  46 9,715 9.8 Python

  🦉Data Version Control | Git for Data & Models | ML Experiments Management

  

  Consideration Number #2: Consider using model life cycle development and management platforms like MLflow, DVC, Weights & Biases, or SageMaker Studio. And Ray, Ray Tune, Ray Train (formerly Ray SGD), PyTorch and TensorFlow for distributed, compute-intensive and deep learning ML workloads.

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