kedro-mlflow
0.5.0
  • Introduction
    • Kedro vs Mlflow
      • What is Kedro?
      • What is Mlflow?
      • A brief comparison between Kedro and Mlflow
        • Configuration and prototyping: Kedro 1 - 0 Mlflow
        • Versioning: Kedro 1 - 1 Mlflow
        • Model packaging and service: Kedro 1 - 2 Mlflow
        • Conclusion: Use Kedro and add Mlflow for machine learning projects
    • Motivation behind the plugin
      • When should I use kedro-mlflow?
      • Why should I use kedro-mlflow?
        • Benchmark of existing solutions
        • Enforcing Kedro principles
  • Installation
    • Install the plugin
      • Pre-requisites
        • Create a virtual environment
        • Check your kedro version
      • Install the plugin
        • Install from PyPI
        • Install from sources
      • Check the installation
      • Available commands
    • Setup your kedro project
      • Create a kedro project
      • Activate kedro-mlflow in your kedro project
        • Setting up the kedro-mlflow configuration file
        • Declaring kedro-mlflow hooks
    • Migration guide
      • Migration from 0.3.0 to 0.4.0
        • Catalog entries
        • Hooks
        • KedroPipelineModel
  • Getting Started
    • Goal of the tutorial
    • Create an example project
      • Install the plugin in a virtual environment
      • Install the toy project
        • Installation with kedro>=0.16.3
        • Installation with kedro>=0.16.0, <=0.16.2
      • Install dependencies
    • First steps with ``kedro-mlflow``
      • Initialize kedro-mlflow
      • Run the pipeline
      • Open the UI
        • Parameters versioning
        • Journal information
        • Artifacts
      • Going further
  • Advanced machine learning versioning
    • Configure mlflow
      • Context: mlflow tracking under the hood
      • The mlflow.yml file
        • Configure the tracking server
        • Configure mlflow experiment
        • Configure the run
        • Configure the hooks
        • Configure the user interface
    • Version parameters
      • Automatic parameters versioning
      • How does MlflowNodeHook operates under the hood?
      • Frequently Asked Questions
        • Will parameters be recorded if the pipeline fails during execution?
        • How are parameters detected by the plugin?
        • How can I register a parameter if I use a TemplatedConfigLoader?
    • Version datasets
      • What is artifact tracking?
      • How to version data in a kedro project?
      • Frequently asked questions
        • Can I pass extra parameters to the MlflowArtifactDataSet for finer control?
        • Can I use the MlflowArtifactDataSet in interactive mode?
        • How do I upload an artifact to a non local destination (e.g. an S3 or blog storage)?
        • Can I log an artifact in a specific run?
        • Can I create a remote folder/subfolders architecture to organize the artifacts?
    • Version models
      • What is model tracking?
      • How to track models using MLflow in Kedro project?
      • Frequently asked questions?
        • How is it working under the hood?
        • How can I track a custom MLflow model flavor?
        • How can I save model locally and log it in MLflow in one step?
    • Version metrics
      • What is metric tracking?
      • How to version metrics in a kedro project?
      • How to return metrics from a node?
    • Opening the User Interface
      • The mlflow user interface
      • The kedro-mlflow helper
  • A mlops framework for efficient deployment
    • Why we need a mlops framework for development lifecycle
      • Machine learning deployment is hard because it comes with a lot of constraints and no adequate tooling
        • Identifying the challenges to address when deploying machine learning
        • A comparison between traditional software development and machine learning projects
      • Deployment issues addressed by kedro-mlflow and their solutions
        • Out of scope
        • Issue 1: The training process is poorly reproducible
        • Issue 2: The data scientist and stakeholders focus on training
        • Issue 3: Inference and training are entirely decoupled
        • Issue 4: Data scientists do not handle business objects
        • Overcoming these problems: support an organisational solution with an efficient tool
    • The architecture of a machine learning project
      • Definition: apps of a machine learning projects
      • Difference between an app and a Kedro pipeline
      • Apps development lifecycle in a machine learning project
        • The data scientist creates at least part of the 3 apps
        • The etl_app
        • The ml_app
        • The user_app
    • An efficient tool for model serving and training / inference synchronization
      • Reminder
      • Enforcing these principles with a dedicated tool
        • Synchronizing training and inference pipeline
        • Packaging and serving a Kedro Pipeline
        • kedro-mlflow’s magic: inference autologging
        • Reuse the model in kedro
    • A step by step example
      • 5 mn summary
      • Complete step by step demo project with code
  • Python objects
    • DataSets
      • MlflowArtifactDataSet
      • Models DataSets
        • MlflowModelLoggerDataSet
        • MlflowModelSaverDataSet
    • Hooks
      • MlflowPipelineHook
      • MlflowNodeHook
    • Pipelines
      • PipelineML and pipeline_ml_factory
    • CLI
      • init
      • ui
    • Configuration
kedro-mlflow
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© Copyright 2020, Yolan Honoré-Rougé. Revision 6d7023d7.

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