Tutorial Lengkap AWS SageMaker Feature Store: Manajemen Fitur untuk ML

Amazon SageMaker Feature Store adalah repositori terkelola penuh untuk menyimpan, berbagi, dan mengelola fitur ML. Layanan ini menyediakan penyimpanan terpusat untuk fitur yang dapat digunakan di seluruh training dan inference, memastikan konsistensi dan reusabilitas.

Mengapa Feature Store?

• Konsistensi: Fitur sama untuk training dan inference
• Reusabilitas: Berbagi fitur antar tim dan model
• Versioning: Lacak perubahan fitur seiring waktu
• Latensi rendah: Serving fitur real-time
• Penyimpanan offline: Data historis untuk training

• Feature Groups
• Online Store (real-time)
• Offline Store (batch/training)
• Feature Definitions

Prerequisites

pip install sagemaker boto3 pandas

SageMaker SDK >= 2.0
python -c "import sagemaker; print(sagemaker.version)"

Quick Start

1. Setup

import boto3
import sagemaker
from sagemaker.featurestore.featuregroup import FeatureGroup
import pandas as pd
import time

session = sagemaker.Session()
bucket = session.defaultbucket()

role = sagemaker.getexecutionrole()

region = session.botoregionname


featurestoresession = sagemaker.Session()

import pandas as pd
import numpy as np
from datetime import datetime

Buat sample data pelanggan
customerdata = pd.DataFrame({
    "customerid": [f"C{i:04d}" for i in range(1, 101)],

    "age": np.random.randint(18, 70, 100),
    "income": np.random.randint(30000, 150000, 100),
    "creditscore": np.random.randint(300, 850, 100),
    "accountbalance": np.random.uniform(0, 50000, 100).round(2),

    "numproducts": np.random.randint(1, 5, 100),
    "isactive": np.random.choice([0, 1], 100)

})

Tambahkan event time (wajib untuk Feature Store)
customerdata["eventtime"] = datetime.now().strftime("%Y-%m-%dT%H:%M:%SZ")


print(customerdata.head())

Membuat Feature Groups

1. Definisikan Feature Group

from sagemaker.featurestore.featuredefinition import (
    FeatureDefinition,
    FeatureTypeEnum
)

Nama feature group
featuregroupname = "customer-features"

Buat feature group
customerfeaturegroup = FeatureGroup(
    name=featuregroupname,
    sagemakersession=featurestoresession

)

Load definisi fitur dari DataFrame
customerfeaturegroup.loadfeaturedefinitions(dataframe=customerdata)


Atau definisikan manual
featuredefinitions = [
    FeatureDefinition(featurename="customerid", featuretype=FeatureTypeEnum.STRING),

    FeatureDefinition(featurename="age", featuretype=FeatureTypeEnum.INTEGRAL),

    FeatureDefinition(featurename="income", featuretype=FeatureTypeEnum.INTEGRAL),

    FeatureDefinition(featurename="creditscore", featuretype=FeatureTypeEnum.INTEGRAL),
    FeatureDefinition(featurename="accountbalance", featuretype=FeatureTypeEnum.FRACTIONAL),

    FeatureDefinition(featurename="numproducts", featuretype=FeatureTypeEnum.INTEGRAL),
    FeatureDefinition(featurename="isactive", featuretype=FeatureTypeEnum.INTEGRAL),

    FeatureDefinition(featurename="eventtime", featuretype=FeatureTypeEnum.STRING)
]

2. Buat Feature Group

# Buat feature group dengan online dan offline stores
customerfeaturegroup.create(
    s3uri=f"s3://{bucket}/feature-store/",

    recordidentifiername="customerid",
    eventtimefeaturename="eventtime",
    rolearn=role,

    enableonlinestore=True,

    description="Fitur pelanggan untuk prediksi churn"
)

Tunggu feature group selesai dibuat
status = customerfeaturegroup.describe().get("FeatureGroupStatus")

while status == "Creating":
    print(f"Status: {status}")
    time.sleep(5)