kailash-ml 0.1.0

Machine learning lifecycle for the Kailash ecosystem

kailash-ml

Classical and deep learning lifecycle for the Kailash ecosystem. Train models, store features, serve predictions, monitor drift, and optionally augment with AI agents.

Part of the Kailash Python SDK by the Terrene Foundation.

Installation

pip install kailash-ml            # Base (~195MB): sklearn, LightGBM, polars
pip install kailash-ml[dl]        # + PyTorch, Lightning
pip install kailash-ml[rl]        # + Stable Baselines3, Gymnasium
pip install kailash-ml[agents]    # + Kaizen (LLM-augmented ML)
pip install kailash-ml[full]      # Everything

Quick Start

import polars as pl
from kailash_ml.engines.feature_store import FeatureStore
from kailash_ml.engines.model_registry import ModelRegistry
from kailash_ml.engines.training_pipeline import TrainingPipeline, ModelSpec, EvalSpec
from kailash_ml.engines.inference_server import InferenceServer
from kailash_ml._types import FeatureSchema, FeatureField
from dataflow import DataFlow

# Initialize
db = DataFlow("sqlite:///ml.db")
await db.initialize()

feature_store = FeatureStore(db)
registry = ModelRegistry(db)
pipeline = TrainingPipeline(feature_store, registry)

# Define schema
schema = FeatureSchema("customers", [
    FeatureField("age", "float64"),
    FeatureField("income", "float64"),
], entity_id_column="customer_id")

# Train
data = pl.DataFrame({"customer_id": ["c1","c2","c3"], "age": [25.0,35.0,45.0], "income": [50000.0,75000.0,90000.0], "churn": [0,1,0]})
result = await pipeline.train(
    data, schema,
    ModelSpec("sklearn.ensemble.RandomForestClassifier", {"n_estimators": 100}, "sklearn"),
    EvalSpec(metrics=["accuracy", "f1"]),
    "churn_predictor",
)
print(f"Accuracy: {result.metrics['accuracy']:.2f}")

# Serve predictions
server = InferenceServer(registry)
predictions = await server.predict("churn_predictor", data)

Engines

kailash-ml provides 9 engines organized in 3 quality tiers:

P0: Production (stable API, full test coverage)

Engine	Purpose
FeatureStore	Compute, version, and serve features with point-in-time correct retrieval
ModelRegistry	Model lifecycle management (staging -> shadow -> production -> archived)
TrainingPipeline	Full training lifecycle: data prep -> train -> evaluate -> register
InferenceServer	Load, cache, and serve predictions; auto-expose via Nexus
DriftMonitor	Feature drift (PSI, KS-test) and performance degradation detection

P1: Production with Caveats (tested, API may evolve)

Engine	Purpose
HyperparameterSearch	Grid, random, Bayesian (optuna), successive halving
AutoMLEngine	Automated model selection with optional LLM augmentation

P2: Experimental (functional, API may change)

Engine	Purpose
DataExplorer	Statistical profiling with polars, plotly visualizations
FeatureEngineer	Automated feature generation and selection

Quality Tier Promotion

• P2 -> P1: 3 integration tests, 2 real-world user validations, no open bugs above LOW
• P1 -> P0: No API changes for 3+ minor releases, complete documentation, performance benchmarks

Agents (Optional)

6 Kaizen agents provide LLM-augmented ML workflows. Double opt-in: install kailash-ml[agents] AND pass agent=True.

Agent	Purpose	Tier
DataScientistAgent	Data analysis + ML strategy	P0
RetrainingDecisionAgent	Retrain/monitor/rollback decision	P0
ModelSelectorAgent	Model family + config recommendation	P1
ExperimentInterpreterAgent	Trial result analysis + next steps	P1
FeatureEngineerAgent	Feature design and pruning	P2
DriftAnalystAgent	Drift interpretation + action decision	P2

All agents emit self_assessed_confidence (0-1), enforce cost budgets, require human approval by default, show baseline comparison, and log decisions to DataFlow.

ONNX Bridge

Models registered in ModelRegistry are automatically exported to ONNX format when supported:

• sklearn: ~90% success rate (all standard estimators)
• LightGBM: ~95% success rate
• PyTorch: 70-85% (feedforward networks)

Failed ONNX exports are non-fatal -- the model falls back to native Python inference. Use model.onnx_status to check export status.

polars-native

All data operations use polars DataFrames internally. The kailash_ml.interop module provides converters for ecosystem integration:

from kailash_ml.interop import to_sklearn_input, to_pandas, from_pandas

X, y = to_sklearn_input(polars_df, label_col="target")
pandas_df = to_pandas(polars_df)
polars_df = from_pandas(pandas_df)

DataFlow Integration

All engines persist data through DataFlow -- no additional infrastructure needed. Features, model metadata, drift reports, and audit logs use the same database as your application.

License

Apache-2.0