v0.3.0

ML Models

Manage machine learning experiments, model registry, and inference through MLflow integration.

Overview

FacilFlow integrates with MLflow to provide a complete ML lifecycle:

  • Track experiments and runs
  • Register and version models
  • Manage staging transitions with approval workflows
  • Run inference in pipelines via the ML Inference node

Experiments

Navigate to Build > ML Models to see experiments.

Creating an Experiment

  1. Click New Experiment
  2. Provide a name and optional description
  3. Select the artifact storage location

Viewing Runs

Each experiment contains runs. A run tracks:

Field Description
Parameters Hyperparameters used for training
Metrics Evaluation metrics (accuracy, RMSE, etc.)
Artifacts Model files, plots, logs
Tags Custom metadata
Duration Training time

Compare runs side-by-side to find the best performing model.

Model Registry

Promote a run’s model to the registry for versioning and deployment.

Registering a Model

  1. Open a run with a logged model
  2. Click Register Model
  3. Choose an existing registered model name or create a new one
  4. The model is added as a new version

Model Versions

Each registered model tracks versions with:

  • Version number (auto-incremented)
  • Source run link
  • Stage (see below)
  • Description

Staging Workflow

Models move through stages:

Stage Description
None Newly registered, not yet reviewed
Staging Under evaluation and testing
Production Approved for production inference
Archived Retired from active use

Transitioning Stages

  1. Open a model version
  2. Click Transition Stage
  3. Select the target stage
  4. Add a comment explaining the transition

Approval Workflows

Transitions to Production require approval:

  1. A user requests the transition
  2. Designated approvers are notified
  3. An approver reviews the model metrics and approves or rejects
  4. On approval, the model moves to Production

Feature Engineering

The Feature Engineer pipeline node computes derived features for ML:

Feature Type Examples
Rolling statistics Mean, std, min, max over a window
Lag features Value at t-1, t-5, t-60
Ratios Feature A / Feature B
Time-based Hour of day, day of week, is_weekend
Aggregates Count, sum over grouped windows

Configure feature engineering in the pipeline builder by adding a Feature Engineer node between your source and ML Inference nodes.

Inference

Use registered models for real-time scoring in pipelines:

  1. Add an ML Inference node to your pipeline
  2. Select the registered model and version (or use “Production” to auto-select)
  3. Map input features from upstream nodes to model inputs
  4. The node outputs predictions downstream

The inference node loads the model from MLflow and applies it to each incoming data batch.

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