Seldon —MLOps

  1. Data Scientist prepares Machine learning model either by training the new model or fine-tuning the pre-trained model with data.
  2. The trained is saved in the storage location may be in any cloud storage as required.
  3. Then one has to create an inference class to read the model from the storage and provide a prediction.
  4. Now using Seldon core build a reusable model server image(docker image) and upload that to the registry.
  5. This model server provides REST or GRPC microservice for scoring.
  6. This can be deployed to the cluster for batch data or streaming data.
  1. All the hard work is done in the Seldon platform.
  2. Seldon provides very complex inference graphs where one can do even A/B testing, break the inference into different modules, etc. The below figure shows where one can even set up a simple Seldon core Inference Graph or a complex Graph with Multi-Arm Bandit, outlier detection, and others.
Image source: Seldon
Image source: Seldon
Image source: Seldon

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Data Scientist | ML-Ops| https://abhi-gm.github.io/ | https://www.linkedin.com/in/abhishek-g-m/ | https://github.com/abhi-gm

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Abhishek Maheshwarappa

Abhishek Maheshwarappa

Data Scientist | ML-Ops| https://abhi-gm.github.io/ | https://www.linkedin.com/in/abhishek-g-m/ | https://github.com/abhi-gm

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