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 Component	Function	Use Case
Tracking	Log Parameters, Metrics	Experiment Comparison
Projects	Package Code + Environment	Reproducibility
Models	Standard Model Format	Multi-framework Deploy
Registry	Model Versioning	Stage Management

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

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"),

}

mlflow.log_metrics(metrics)

# Log Model

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

# Log Artifacts

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

print(f"Run ID: {mlflow.active_run().info.run_id}")

print(f"Accuracy: {metrics['accuracy']:.4f}")

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",

{"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"),

]

print("=== MLflow Experiment Runs ===")

print(f"{'Name':<16} {'Model':<14} {'Accuracy':>8} {'F1':>6} {'Status'}")

for r in runs:

print(f" {r.name:<16} {r.model:<14} {r.metrics['accuracy']:>8.3f} "

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

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

def objective(trial):

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))

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

class ModelStage(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"),

]

print("\n=== Model Registry ===")

for m in models:

print(f" {m.name} v{m.version} [{m.stage.value}] "

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

Conference Preparation

# === Tech Conference Preparation ===

@dataclass
class ConferenceTalk:
    title: str
    abstract: str
    duration_min: int
    slides: int
    demo: bool
    audience: str

@dataclass
class SlideOutline:
    section: str
    slides: int
    content: str

talk = ConferenceTalk(
    title="Scaling ML Experiments with MLflow: From Notebook to Production",
    abstract="How we scaled from ad-hoc notebooks to a systematic ML experimentation platform using MLflow",
    duration_min=30,
    slides=25,
    demo=True,
    audience="ML Engineers, Data Scientists",
)

outline = [
    SlideOutline("Introduction", 3, "Problem Statement, Why Experiment Tracking"),
    SlideOutline("MLflow Overview", 4, "Components, Architecture, Setup"),
    SlideOutline("Live Demo", 5, "Tracking, Compare Runs, Registry"),
    SlideOutline("Production Setup", 5, "Docker, Auth, Storage, CI/CD"),
    SlideOutline("Results", 4, "Before/After Metrics, Team Productivity"),
    SlideOutline("Lessons Learned", 2, "Pitfalls, Best Practices"),
    SlideOutline("Q&A", 2, "Questions and Discussion"),
]

print(f"=== Conference Talk ===")
print(f"  Title: {talk.title}")
print(f"  Duration: {talk.duration_min} min | Slides: {talk.slides}")
print(f"  Demo: {'Yes' if talk.demo else 'No'} | Audience: {talk.audience}")
print(f"\n  Slide Outline:")
total_slides = 0
for section in outline:
    total_slides += section.slides
    print(f"    [{section.section}] {section.slides} slides — {section.content}")
print(f"\n  Total: {total_slides} slides")

# Presentation Tips
tips = {
    "Content": [
        "เริ่มด้วย Problem ที่ผู้ฟังเข้าใจ",
        "แสดง Before/After ชัดเจน",
        "Demo สดที่ทำงานจริง มี Backup Video",
        "แชร์ตัวเลข Impact ที่วัดได้",
    ],
    "Delivery": [
        "ซ้อมพูด 3-5 รอบ จับเวลา",
        "พูดช้าลง หยุดหายใจ",
        "สบตาผู้ฟัง ไม่อ่าน Slides",
        "เตรียม Q&A รู้จุดอ่อนของงาน",
    ],
    "Slides": [
        "1 Slide = 1 Idea ไม่ยัดเยียด",
        "ใช้ภาพและ Diagram มากกว่าข้อความ",
        "Font ใหญ่ อ่านได้จากหลังห้อง",
        "Code Snippet สั้น Highlight จุดสำคัญ",
    ],
}

print(f"\n\nPresentation Tips:")
for category, items in tips.items():
    print(f"\n  [{category}]")
    for tip in items:
        print(f"    - {tip}")

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