A Framework for Production-Ready Continuous Machine Learning

Related tags

Machine learning machine-learning deep-learning artificial-intelligence mlops continuous-machine-learning
Overview

CML

"Domain generalization is dead, Continuous Machine Learning lives forever." —— an iKun

CML is a framework for production-ready continuous machine learning in pure Rust.

Test Status Coverage Status

Motivation

Domain-specific problem are widely prevalent in real-world scenarios.

To address this problem:

Traditional method CML method
  • Increasing the amount of training data
  • Optimizing model architecture
  • Applying diverse data augmentation techniques
 Combining Continuous Integration
with Machine Learning

How does it work?

See our architecture document.

Contributing

There is currently no code of conduct document available. However, we highly recommend that you read our architecture document first and foremost.

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Comments
  • docs(inference): Add the sequence diagram for Inference trait

    docs(inference): Add the sequence diagram for Inference trait

    Please show me the rendered diagram here.

    sequenceDiagram
    participant Source
    box rgb(255, 144, 0) CML
    participant CML Core
    participant Dataset
    end
    Source --) CML Core: Arbitrary method of data transmission
    Database ->> CML Core : Get the model information of the batch
    CML Core ->> CML Core : Inference data
    loop Each inference result
        CML Core ->> Dataset: Add 1 ns as new timestamp
    end
    Dataset--)Database: Exec insertion statements

    Resolves #4

    opened by lazyky 1
  • Add/polish schematic diagrams

    Add/polish schematic diagrams

    To compare the traditional method with our CML framework, we need two schematic diagrams:

    image

    I've provided an draft as example in the above image, please give a polish to the left one and draw a new right one.

    The new diagrams should be PRed as files traditional.png and new.png in assets directory at the root of the project.

    image

    opened by bioinformatist 0
  • Finish all unit test code

    Finish all unit test code

    Currently we have only less than 70% line coverage which is reported by Tarpaulin.

    In this task:

    1. The remaining parts need to be covered ASAP
    2. The environment should be cleaned(e.g. remove the files/directories produced by test) every time

    To run test with Tarpaulin:

    cargo tarpaulin -- --test-threads=1 --nocapture

    You can use breakpoint as well as Rust's dbg!() macro for debugging use.

    opened by bioinformatist 0
Owner
Yu Sun
Rustacean | Data Scientist | Web Full Stack | (Operation & Maintenance) | Bioinformatics
Yu Sun
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