feat: Add MLflow integration utilities

- client: Connection management and helpers
- tracker: General experiment tracking
- inference_tracker: Async metrics for NATS handlers
- model_registry: Model registration with KServe metadata
- kfp_components: Kubeflow Pipeline components
- experiment_comparison: Run comparison tools
- cli: Command-line interface

README.md

@@ -1,2 +1,129 @@
-# mlflow
+# MLflow Utils
 
+MLflow integration utilities for the DaviesTechLabs AI/ML platform.
+
+## Installation
+
+```bash
+pip install -r requirements.txt
+```
+
+Or from Gitea:
+```bash
+pip install git+https://git.daviestechlabs.io/daviestechlabs/mlflow.git
+```
+
+## Modules
+
+| Module | Description |
+|--------|-------------|
+| `client.py` | MLflow client configuration and helpers |
+| `tracker.py` | General MLflowTracker for experiments |
+| `inference_tracker.py` | Async inference metrics for NATS handlers |
+| `model_registry.py` | Model Registry with KServe metadata |
+| `kfp_components.py` | Kubeflow Pipeline MLflow components |
+| `experiment_comparison.py` | Compare experiments and runs |
+| `cli.py` | Command-line interface |
+
+## Quick Start
+
+```python
+from mlflow_utils import get_mlflow_client, MLflowTracker
+
+# Simple tracking
+with MLflowTracker(experiment_name="my-experiment") as tracker:
+    tracker.log_params({"learning_rate": 0.001})
+    tracker.log_metrics({"accuracy": 0.95})
+```
+
+## Inference Tracking
+
+For NATS handlers (chat-handler, voice-assistant):
+
+```python
+from mlflow_utils import InferenceMetricsTracker
+from mlflow_utils.inference_tracker import InferenceMetrics
+
+tracker = InferenceMetricsTracker(
+    experiment_name="voice-assistant-prod",
+    batch_size=100,  # Batch metrics before logging
+)
+
+# During request handling
+metrics = InferenceMetrics(
+    request_id="uuid",
+    total_latency=1.5,
+    llm_latency=0.8,
+    input_tokens=150,
+    output_tokens=200,
+)
+await tracker.log_inference(metrics)
+```
+
+## Model Registry
+
+Register models with KServe deployment metadata:
+
+```python
+from mlflow_utils.model_registry import register_model_for_kserve
+
+register_model_for_kserve(
+    model_name="my-qlora-adapter",
+    model_uri="runs:/abc123/model",
+    kserve_runtime="kserve-vllm",
+    gpu_type="amd-strixhalo",
+)
+```
+
+## Kubeflow Components
+
+Use in KFP pipelines:
+
+```python
+from mlflow_utils.kfp_components import (
+    log_experiment_component,
+    register_model_component,
+)
+```
+
+## CLI
+
+```bash
+# List experiments
+python -m mlflow_utils.cli list-experiments
+
+# Compare runs
+python -m mlflow_utils.cli compare-runs --experiment "qlora-training"
+
+# Export metrics
+python -m mlflow_utils.cli export --run-id abc123 --output metrics.json
+```
+
+## Configuration
+
+| Environment Variable | Default | Description |
+|---------------------|---------|-------------|
+| `MLFLOW_TRACKING_URI` | `http://mlflow.mlflow.svc.cluster.local:80` | MLflow server |
+| `MLFLOW_EXPERIMENT_NAME` | `default` | Default experiment |
+| `MLFLOW_ENABLE_ASYNC` | `true` | Async logging for handlers |
+
+## Module Structure
+
+```
+mlflow_utils/
+├── __init__.py              # Public API
+├── client.py                # Connection management
+├── tracker.py               # General experiment tracker
+├── inference_tracker.py     # Async inference metrics
+├── model_registry.py        # Model registration + KServe
+├── kfp_components.py        # Kubeflow components
+├── experiment_comparison.py # Run comparison tools
+└── cli.py                   # Command-line interface
+```
+
+## Related
+
+- [handler-base](https://git.daviestechlabs.io/daviestechlabs/handler-base) - Uses inference tracker
+- [kubeflow](https://git.daviestechlabs.io/daviestechlabs/kubeflow) - KFP components
+- [argo](https://git.daviestechlabs.io/daviestechlabs/argo) - Training workflows
+- [homelab-design](https://git.daviestechlabs.io/daviestechlabs/homelab-design) - Architecture docs