Tutorial Lengkap Vertex AI: Platform ML Terpadu di Google Cloud

Vertex AI adalah platform machine learning terpadu Google Cloud yang menggabungkan semua layanan ML Google Cloud. Platform ini menyediakan tools untuk membangun, mendeploy, dan menskalakan model ML dengan AutoML dan custom training.

Mengapa Vertex AI?

• Platform terpadu: Semua tools ML dalam satu tempat
• AutoML: Pembuatan model tanpa kode
• Custom training: Kontrol penuh dengan kode custom
• MLOps: Built-in pipelines dan monitoring
• Scalable: Infrastruktur enterprise-grade

• Datasets
• Training (AutoML dan Custom)
• Model Registry
• Endpoints
• Pipelines
• Feature Store
• Experiments

Prerequisites

pip install google-cloud-aiplatform

Autentikasi
gcloud auth login
gcloud config set project your-project-id

Setup

1. Inisialisasi Vertex AI

from google.cloud import aiplatform

aiplatform.init(
    project="your-project-id",
    location="us-central1",
    stagingbucket="gs://your-bucket"

)

gcloud services enable aiplatform.googleapis.com
gcloud services enable compute.googleapis.com
gcloud services enable storage.googleapis.com

from google.cloud import aiplatform

Buat dari BigQuery
dataset = aiplatform.TabularDataset.create(
    displayname="customer-churn-dataset",
    bqsource="bq://project.dataset.table"

)

Buat dari GCS
dataset = aiplatform.TabularDataset.create(
    displayname="customer-churn-dataset",
    gcssource="gs://bucket/data/train.csv"

)

print(f"Dataset dibuat: {dataset.resourcename}")

2. Buat Image Dataset

# Buat image dataset
imagedataset = aiplatform.ImageDataset.create(

    displayname="product-images",
    gcssource="gs://bucket/images/",

    importschemauri=aiplatform.schema.dataset.ioformat.image.singlelabelclassification

)

# Buat text dataset
textdataset = aiplatform.TextDataset.create(
    displayname="sentiment-dataset",

    gcssource="gs://bucket/text/data.jsonl",
    importschemauri=aiplatform.schema.dataset.ioformat.text.singlelabelclassification
)

AutoML Training

1. AutoML Tabular

# Buat AutoML tabular training job
job = aiplatform.AutoMLTabularTrainingJob(
    displayname="churn-automl",

    optimizationpredictiontype="classification",

    optimizationobjective="maximize-au-roc"
)

Train model
model = job.run(
    dataset=dataset,
    targetcolumn="churn",

    trainingfractionsplit=0.8,

    validationfractionsplit=0.1,

    testfractionsplit=0.1,

    budgetmillinodehours=1000,
    modeldisplayname="churn-model"
)

print(f"Model ditraining: {model.resourcename}")

# Buat AutoML image training job
job = aiplatform.AutoMLImageTrainingJob(
    displayname="image-classifier",
    predictiontype="classification",

    multilabel=False
)

Train model
model = job.run(
    dataset=imagedataset,

    trainingfractionsplit=0.8,

    validationfractionsplit=0.1,

    testfractionsplit=0.1,

    budgetmillinodehours=8000,
    modeldisplayname="product-classifier"
)

3. AutoML Text Classification

# Buat AutoML text training job
job = aiplatform.AutoMLTextTrainingJob(
    displayname="sentiment-classifier",

    predictiontype="classification",
    multilabel=False

)

Train model
model = job.run(
    dataset=textdataset,