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W&B MLOps

Weights Biases Career Development IT MLOps Experiment Tracking Model Registry Hyperparameter Sweep Artifacts Reports Portfolio Resume Production ML

Experiment Tracking

=== W&B Experiment Tracking ===

pip install wandb torch torchvision

import wandb

import torch

import torch.nn as nn

# Initialize

wandb.init(

project="image-classifier",

name="resnet50-baseline",

config={

"architecture": "ResNet50",

"dataset": "CIFAR-10",

"epochs": 50,

"batch_size": 32,

"learning_rate": 0.001,

"optimizer": "Adam",

"weight_decay": 1e-4,

}

)

config = wandb.config

# Training Loop

for epoch in range(config.epochs):

model.train()

for batch_idx, (data, target) in enumerate(train_loader):

optimizer.zero_grad()

output = model(data)

loss = criterion(output, target)

เนื้อหาเกี่ยวข้อง — แนะนำให้อ่าน A/B Testing ML Capacity Planning

loss.backward()

optimizer.step()

wandb.log({

"train/loss": loss.item(),

"train/batch": batch_idx,

"epoch": epoch,

})

แนะนำเพิ่มเติม — SiamCafeBook

# Validation

val_loss, val_acc = evaluate(model, val_loader)

wandb.log({

"val/loss": val_loss,

"val/accuracy": val_acc,

"epoch": epoch,

"learning_rate": optimizer.param_groups[0]["lr"],

})

# Log model checkpoint

if val_acc > best_acc:

wandb.save("best_model.pth")

best_acc = val_acc

wandb.finish()

from dataclasses import dataclass

@dataclass

class ExperimentRun:

run_name: str

model: str

lr: float

batch_size: int

เนื้อหาเกี่ยวข้อง — ทำความเข้าใจ phishing scams คือ — ข้อมูลครบถ้วน 2026

val_acc: float

val_loss: float

epochs: int

duration: str

runs = [

ExperimentRun("resnet50-baseline", "ResNet50", 0.001, 32, 0.923, 0.245, 50, "2h 15m"),

ExperimentRun("resnet50-lr-high", "ResNet50", 0.01, 32, 0.891, 0.312, 50, "2h 10m"),

ExperimentRun("resnet50-batch64", "ResNet50", 0.001, 64, 0.918, 0.258, 50, "1h 45m"),

ExperimentRun("efficientnet-b0", "EfficientNet-B0", 0.001, 32, 0.935, 0.198, 50, "3h 20m"),

ExperimentRun("vit-small", "ViT-Small", 0.0003, 32, 0.941, 0.182, 100, "5h 40m"),

]

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

for r in runs:

print(f" [{r.run_name}] Model: {r.model}")

print(f" LR: {r.lr} | BS: {r.batch_size} | Epochs: {r.epochs}")

แนะนำเพิ่มเติม — สัญญาณเทรดรายวัน XM Signal

print(f" Val Acc: {r.val_acc:.3f} | Val Loss: {r.val_loss:.3f} | Time: {r.duration}")

Hyperparameter Sweep

=== W&B Sweep Configuration ===

sweep_config = {

"method": "bayes", # grid, random, bayes

"metric": {

"name": "val/accuracy",

"goal": "maximize"

},

"parameters": {

"learning_rate": {

"min": 0.0001,

"max": 0.01,

เนื้อหาเกี่ยวข้อง — อ่านต่อ: prompt engineering หมายถึงอะไร

"distribution": "log_uniform_values"

},

"batch_size": {

"values": [16, 32, 64, 128]

},

"optimizer": {

"values": ["adam", "sgd", "adamw"]

},

"weight_decay": {

"min": 1e-5,

"max": 1e-2,

"distribution": "log_uniform_values"

},

"dropout": {

"min": 0.1,

"max": 0.5

}

},

"early_terminate": {

"type": "hyperband",

"min_iter": 10,

"eta": 3

}

}

sweep_id = wandb.sweep(sweep_config, project="image-classifier")

เนื้อหาเกี่ยวข้อง — แนะนำให้อ่าน ฮาร์ดดิส ssd คือ — ข้อมูลครบถ้วน 2026

wandb.agent(sweep_id, function=train, count=50)

@dataclass

class SweepResult:

rank: int

lr: float

batch_size: int

optimizer: str

dropout: float

val_acc: float

results = [

SweepResult(1, 0.00032, 32, "adamw", 0.25, 0.945),

SweepResult(2, 0.00045, 64, "adam", 0.20, 0.941),

SweepResult(3, 0.00028, 32, "adamw", 0.30, 0.939),

SweepResult(4, 0.001, 32, "adam", 0.15, 0.935),

SweepResult(5, 0.0005, 128, "sgd", 0.20, 0.928),

]

print("\n=== Top 5 Sweep Results ===")

for r in results:

print(f" Rank {r.rank}: Val Acc {r.val_acc:.3f}")

print(f" LR: {r.lr} | BS: {r.batch_size} | Opt: {r.optimizer} | Drop: {r.dropout}")

Career Portfolio

# === Building ML Career Portfolio ===

@dataclass
class PortfolioProject:
    project: str
    tech_stack: str
    wandb_features: str
    skill_demonstrated: str
    portfolio_url: str

projects = [
    PortfolioProject("Image Classification", "PyTorch ResNet EfficientNet",
        "Experiment Tracking, Sweep, Model Registry",
        "Deep Learning, Hyperparameter Tuning, MLOps",
        "wandb.ai/username/image-classifier"),
    PortfolioProject("NLP Sentiment Analysis", "HuggingFace Transformers BERT",
        "Fine-tuning Tracking, Dataset Versioning",
        "NLP, Transfer Learning, Data Pipeline",
        "wandb.ai/username/sentiment"),
    PortfolioProject("Object Detection", "YOLOv8 COCO Dataset",
        "mAP Tracking, Confusion Matrix, Media Panel",
        "Computer Vision, Model Evaluation",
        "wandb.ai/username/yolo-detection"),
    PortfolioProject("Time Series Forecast", "Prophet LSTM Transformer",
        "Multi-model Comparison, Reports",
        "Time Series, Feature Engineering",
        "wandb.ai/username/forecasting"),
]

print("ML Career Portfolio:")
for p in projects:
    print(f"  [{p.project}] Stack: {p.tech_stack}")
    print(f"    W&B: {p.wandb_features}")
    print(f"    Skills: {p.skill_demonstrated}")
    print(f"    URL: {p.portfolio_url}")

career_path = {
    "Junior ML Engineer": "W&B basics, Experiment Tracking, 2-3 projects",
    "ML Engineer": "Sweeps, Model Registry, CI/CD, 5+ projects",
    "Senior ML Engineer": "Full MLOps pipeline, Team collaboration, Reports",
    "ML Architect": "Platform design, W&B Server self-hosted, Enterprise",
    "Research Scientist": "Advanced experiments, Paper-quality Reports",
}

print(f"\n\nCareer Path:")
for k, v in career_path.items():
    print(f"  [{k}]: {v}")

เคล็ดลับ

• wandb.init: ตั้ง Project Name และ Config ทุกครั้ง
• Sweep: ใช้ Bayes Method สำหรับ Sweep ประสิทธิภาพดีกว่า Random
• Report: สร้าง W&B Report แชร์ผลงานแทน Notebook
• Portfolio: ใช้ W&B Public Profile เป็น ML Portfolio
• Community: เข้าร่วม W&B Community เรียนรู้จากู้คืนอื่น

การนำความรู้ไปประยุกต์ใช้งานจริง

แหล่งเรียนรู้ที่แนะนำ ได้แก่ Official Documentation ที่อัพเดทล่าสุดเสมอ Online Course จาก Coursera Udemy edX ช่อง YouTube คุณภาพทั้งไทยและอังกฤษ และ Community อย่าง Discord Reddit Stack Overflow ที่ช่วยแลกเปลี่ยนประสบการณ์กับนักพัฒนาทั่วโลก

สรุป

Weights Biases Career Development IT MLOps Experiment Tracking Model Registry Hyperparameter Sweep Portfolio Resume Reports Community Production ML