Chapter 10 of 12

Cloud AI Platforms

Compare AWS SageMaker, GCP Vertex AI, and Azure ML with hands-on examples and certification paths.

Cloud AI Platform Comparison

Feature	AWS SageMaker	GCP Vertex AI	Azure ML
Notebooks	SageMaker Studio	Workbench	Azure ML Studio
AutoML	Autopilot	AutoML Tables	AutoML
Feature Store	SM Feature Store	Vertex FS	Managed FS
Model Registry	Model Registry	Model Registry	Model Registry
LLM Access	Bedrock	Model Garden	Azure OpenAI
Data Integration	S3, Redshift, Glue	BigQuery, GCS	ADLS, Synapse
Strength	Broadest ecosystem	Best for BQ users	Best for enterprise

AWS SageMaker Quick Start

import sagemaker
from sagemaker.sklearn import SKLearn

estimator = SKLearn(
    entry_point="train.py",
    role=sagemaker.get_execution_role(),
    instance_type="ml.m5.xlarge",
    framework_version="1.2-1",
    hyperparameters={"n_estimators": 200, "max_depth": 4},
)

estimator.fit({"train": "s3://bucket/train.csv"})
predictor = estimator.deploy(instance_type="ml.t2.medium")

GCP Vertex AI Quick Start

from google.cloud import aiplatform

aiplatform.init(project="my-project", location="us-central1")

job = aiplatform.CustomTrainingJob(
    display_name="churn-training",
    script_path="train.py",
    container_uri="us-docker.pkg.dev/vertex-ai/training/sklearn-cpu.1-0:latest",
)

model = job.run(model_display_name="churn-predictor", machine_type="n1-standard-4")
endpoint = model.deploy(machine_type="n1-standard-2", min_replica_count=1)

Certifications

Cert	Cloud	Difficulty	Prep Time
AWS ML Specialty	AWS	Hard	8-12 weeks
GCP ML Engineer	GCP	Hard	8-12 weeks
Azure AI Engineer	Azure	Medium	6-8 weeks

Strategy

Pick the cloud you already know. Your existing cloud skills cut prep time by 40-50%.

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