MLflow Experiment Tech Conference 2026 — จัดการ

MLflow Experiment Tech Conference

MLflow Open Source ML Lifecycle Tracking Projects Models Registry Experiment Parameters Metrics Artifacts Tech Conference Presentation Demo

MLflow Tracking Setup

=== MLflow Experiment Tracking ===

อ่านเพิ่ม: LLM Fine-tuning LoRA — วิธีตั้งค่าและใช้งานจริงพร้อมตัวอย่าง · อ่านเพิ่ม: LocalAI Self-hosted Compliance Automation — วิธีตั้งค่าและใช · อ่านเพิ่ม: deep learning vs machine learning คือ — วิธีตั้งค่าและใช้งาน

pip install mlflow scikit-learn pandas

import mlflow

import mlflow.sklearn

from sklearn.ensemble import RandomForestClassifier

from sklearn.model_selection import train_test_split

from sklearn.metrics import accuracy_score, f1_score, precision_score

import pandas as pd

# Set Tracking URI

mlflow.set_tracking_uri("http://localhost:5000")

mlflow.set_experiment("image-classifier-v2")

# Load Data

df = pd.read_csv("data/train.csv")

X_train, X_test, y_train, y_test = train_test_split(

df.drop("label", axis=1), df["label"],

test_size=0.2, random_state=42

)

# Train with MLflow Tracking

with mlflow.start_run(run_name="rf-baseline"):

# Log Parameters

params = {

"n_estimators": 100,

"max_depth": 10,

"min_samples_split": 5,

"random_state": 42,

}

mlflow.log_params(params)

# Train Model

เนื้อหาเกี่ยวข้อง — แนะนำให้อ่าน CSS Container Queries Load Testing Strategy

model = RandomForestClassifier(**params)

model.fit(X_train, y_train)

# Evaluate

y_pred = model.predict(X_test)

metrics = {

"accuracy": accuracy_score(y_test, y_pred),

"f1_score": f1_score(y_test, y_pred, average="weighted"),

"precision": precision_score(y_test, y_pred, average="weighted"),

แนะนำเพิ่มเติม — บทวิเคราะห์จาก XM Signal

}

mlflow.log_metrics(metrics)

# Log Model

mlflow.sklearn.log_model(model, "model")

# Log Artifacts

mlflow.log_artifact("data/train.csv")

mlflow ui --port 5000

from dataclasses import dataclass, field

from typing import List, Dict

@dataclass

class ExperimentRun:

run_id: str

name: str

params: Dict

metrics: Dict

model: str

status: str

runs = [

ExperimentRun("run-001", "rf-baseline",

เนื้อหาเกี่ยวข้อง — แนะนำให้อ่าน 1 pi network เท่ากับกี่บาท

{"n_estimators": 100, "max_depth": 10},

{"accuracy": 0.892, "f1": 0.887}, "RandomForest", "completed"),

ExperimentRun("run-002", "rf-tuned",

{"n_estimators": 500, "max_depth": 20},

{"accuracy": 0.921, "f1": 0.918}, "RandomForest", "completed"),

ExperimentRun("run-003", "xgb-baseline",

{"n_estimators": 200, "learning_rate": 0.1},

{"accuracy": 0.935, "f1": 0.932}, "XGBoost", "completed"),

ExperimentRun("run-004", "xgb-tuned",

{"n_estimators": 500, "learning_rate": 0.05},

{"accuracy": 0.948, "f1": 0.945}, "XGBoost", "completed"),

ExperimentRun("run-005", "neural-net",

{"hidden_layers": 3, "learning_rate": 0.001},

{"accuracy": 0.952, "f1": 0.949}, "PyTorch", "completed"),

]

แนะนำเพิ่มเติม — อ่านเพิ่มเติมที่ SiamCafeBook

f"{r.metrics['f1']:>6.3f} {r.status}")

Model Registry

=== MLflow Model Registry ===

import mlflow

from mlflow.tracking import MlflowClient

client = MlflowClient()

# Register Model

result = mlflow.register_model(

f"runs:/{run_id}/model",

"image-classifier"

)

# Transition to Staging

เนื้อหาเกี่ยวข้อง — อ่านต่อ: Mvrv

client.transition_model_version_stage(

name="image-classifier",

version=result.version,

stage="Staging",

)

# Transition to Production

client.transition_model_version_stage(

name="image-classifier",

version=result.version,

stage="Production",

)

# Load Production Model

model = mlflow.pyfunc.load_model(

"models:/image-classifier/Production"

)

predictions = model.predict(X_test)

Hyperparameter Tuning with MLflow

import optuna

with mlflow.start_run(nested=True):

params = {

"n_estimators": trial.suggest_int("n_estimators", 100, 1000),

"max_depth": trial.suggest_int("max_depth", 5, 30),

"learning_rate": trial.suggest_float("learning_rate", 0.01, 0.3),

"subsample": trial.suggest_float("subsample", 0.6, 1.0),

}

mlflow.log_params(params)

model = XGBClassifier(**params)

model.fit(X_train, y_train)

accuracy = accuracy_score(y_test, model.predict(X_test))

เนื้อหาเกี่ยวข้อง — อ่านต่อ: force ssl with https redirect คือ —

mlflow.log_metric("accuracy", accuracy)

return accuracy

with mlflow.start_run(run_name="optuna-tuning"):

study = optuna.create_study(direction="maximize")

study.optimize(objective, n_trials=50)

mlflow.log_params(study.best_params)

mlflow.log_metric("best_accuracy", study.best_value)

from enum import Enum

NONE = "None"

STAGING = "Staging"

PRODUCTION = "Production"

ARCHIVED = "Archived"

@dataclass

class RegisteredModel:

name: str

version: int

stage: ModelStage

accuracy: float

run_id: str

models = [

RegisteredModel("image-classifier", 1, ModelStage.ARCHIVED, 0.892, "run-001"),

RegisteredModel("image-classifier", 2, ModelStage.ARCHIVED, 0.935, "run-003"),

RegisteredModel("image-classifier", 3, ModelStage.PRODUCTION, 0.952, "run-005"),

RegisteredModel("image-classifier", 4, ModelStage.STAGING, 0.961, "run-006"),

]

f"Accuracy: {m.accuracy:.3f} | Run: {m.run_id}")

เคล็ดลับ

• Tracking: Log ทุก Parameter และ Metric ตั้งแต่เริ่ม
• Naming: ตั้งชื่อ Run ที่สื่อความหมาย ใช้ Tags จัดกลุ่ม
• Registry: ใช้ Stage Management Staging -> Production
• Demo: เตรียม Demo ที่ทำงานจริง มี Backup Video
• GitHub: แชร์ Code ที่ Reproducible บน GitHub

MLflow คืออะไร

Open Source ML Lifecycle Databricks Tracking Projects Models Registry Parameters Metrics Artifacts ทุก Framework