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Machine Learning deployment has many challenges which Seldon Core's goals are to solve, these include:

  • Allow a wide range of ML modelling tools to be easily deployed, e.g. Python, R, Spark, and proprietary models
  • Launch ML runtime graphs, scale up/down, perform rolling updates
  • Run health checks and ensure recovery of failed components
  • Infrastructure optimization for ML
  • Latency optimization
  • Connect to business apps via various APIs, e.g. REST, gRPC
  • Allow construction of Complex runtime microservice graphs
    • Route requests
    • Transform data
    • Ensembles results
  • Allow various deployment modalities
    • Synchronous
    • Asynchronous
    • Batch
  • Allow Auditing and clear versioning
  • Integrate into Continuous Integration (CI)
  • Allow Continuous Deployment (CD)
  • Provide Monitoring
    • Base metrics: Accuracy, request latency and throughput
  • Complex metrics:
    • Concept drift
    • Bias detection
    • Outlier detection
  • Allow for Optimization
    • AB Tests
    • Multi-Armed Bandits

If you see further challenges please add an Issue.

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