Beginner-friendly comparison of the three major cloud ML platforms. Learn which service fits your needs based on pricing, features, ease of use, and integration with your existing stack.
AI Editorial Team
Collective Intelligence
Starting with cloud machine learning can be overwhelming. Each major cloud provider offers a managed ML service with different strengths, pricing models, and learning curves. This beginner's guide compares AWS SageMaker, Azure Machine Learning, and Google Vertex AI to help you make an informed choice for your first project.
Before diving into comparisons, understand why managed services beat DIY infrastructure:
| Feature | AWS SageMaker | Azure Machine Learning | Google Vertex AI |
|---|---|---|---|
| Launch Year | 2017 | 2019 (GA 2020) | 2021 |
| Free Tier | 2 months free (250 hours Studio) | $200 Azure credit | $300 GCP credit |
| Starting Price | $0.10/job hour + instance costs | $0.05/experiment hour + compute | $0.05/job hour + compute |
| Primary Language | Python (boto3 SDK) | Python (azureml SDK) | Python (google-cloud-aiplatform) |
| Notebook Environment | SageMaker Studio | Azure ML Studio | Vertex AI Workbench |
| AutoML Support | SageMaker Autopilot | Automated ML | Vertex AI AutoML |
| Model Registry | SageMaker Model Registry | MLflow integration | Vertex AI Model Registry |
| Best For | AWS ecosystem users | Microsoft/Azure shops | Google/Dataflow users |
Strengths: -H Completeness: Most mature with 50+ integrated features
Getting Started Code:
1import boto3
2import sagemaker
3from sagemaker import Estimator
4from sagemaker.sklearn import SKLearn
5
6# Initialize session
7session = sagemaker.Session()
8role = sagemaker.get_execution_role()
9
10# Create estimator for scikit-learn model
11sklearn_estimator = SKLearn(
12 entry_point='train.py',
13 role=role,
14 instance_count=1,
15 instance_type='ml.m5.large',
16 framework_version='1.0-1',
17 py_version='py3',
18 sagemaker_session=session
19)
20
21# Train model
22sklearn_estimator.fit({'train': 's3://bucket/train.csv'})
23
24# Deploy endpoint
25predictor = sklearn_estimator.deploy(
26 initial_instance_count=1,
27 instance_type='ml.m5.large'
28)
29Pricing Example (Training): -sml.m5.large (2 vCPU, 8GB RAM): $0.115/hour
Strengths:
Getting Started Code:
1from azureml.core import Workspace, Experiment, Environment
2from azureml.core.compute import ComputeTarget
3from azureml.train.sklearn import SKLearn
4
5# Connect to workspace
6ws = Workspace.from_config()
7
8# Create compute target
9compute_target = ComputeTarget.create(
10 ws, 'cpu-cluster',
11 vm_size='STANDARD_D2_V2',
12 min_nodes=0,
13 max_nodes=4
14)
15
16# Define environment
17env = Environment.from_conda_specification(
18 name='sklearn-env',
19 file_path='conda.yml'
20)
21
22# Create estimator
23estimator = SKLearn(
24 source_directory='./src',
25 entry_script='train.py',
26 compute_target=compute_target,
27 environment_definition=env
28)
29
30# Submit experiment
31experiment = Experiment(ws, 'first-experiment')
32run = experiment.submit(estimator)
33run.wait_for_completion()
34Pricing Example (Training):
Strengths:
Getting Started Code:
1from google.cloud import aiplatform
2from google.cloud.aiplatform import gapic as aip
3
4# Initialize Vertex AI
5aiplatform.init(project="your-project", location="us-central1")
6
7# Create a custom training job
8job = aiplatform.CustomTrainingJob(
9 display_name="first-training-job",
10 script_path="train.py",
11 container_uri="us-docker.pkg.dev/vertex-ai/training/tf-cpu.2-8:latest",
12 requirements=["scikit-learn==1.0"],
13 model_serving_container_image_uri="us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-8:latest"
14)
15
16# Run training
17model = job.run(
18 dataset=aiplatform.TabularDataset("projects/your-project/datasets/your-dataset"),
19 model_display_name="first-model",
20 machine_type="n1-standard-4"
21)
22
23# Deploy to endpoint
24endpoint = model.deploy(
25 machine_type="n1-standard-2",
26 min_replica_count=1,
27 max_replica_count=3
28)
29Pricing Example (Training):
Ask these questions to choose:
: Python-heavy: All three are good (Python SDKs available)
All platforms can handle this, but each has a different "easy path":
AWS SageMaker Path:
Azure ML Path:
Vertex AI Path:
Assuming 1,000 predictions/day, 1 hour training/month:
| Platform | Training Cost | Inference Cost | Total Monthly |
|---|---|---|---|
| AWS SageMaker | $0.115 | $0.10 (1K predictions) | $0.215 |
| Azure ML | $0.126 | $0.15 (1K predictions) | $0.276 |
| Vertex AI | $0.190 | $0.25 (1K predictions) | $0.440 |
Note: These are baseline costs. Real projects include storage, networking, and additional services.
All providers offer free credits:
Maximize your free tier:
Start simple, but plan for future:
1# Strategy: Write platform-agnostic training code
2def train_model(data_path, model_type='linear'):
3 # Your model training logic here
4 # Keep it independent of cloud SDKs
5 pass
6
7# Platform-specific deployment wrappers
8def deploy_aws(model):
9 import boto3
10 # AWS-specific deployment
11
12def deploy_azure(model):
13 from azureml.core import Model
14 # Azure-specific deployment
15
16def deploy_gcp(model):
17 from google.cloud import aiplatform
18 # GCP-specific deployment
19All three platforms are excellent choices for beginners. The best choice depends on:
Recommendation for absolute beginners: Start with the platform where you already have an account and some familiarity. The learning curve for cloud ML is steep enough without also learning a new cloud platform.
Remember: The goal isn't to pick the "best" platform, but to pick the platform that gets you from idea to deployed model fastest. You can always migrate later as your needs evolve.
cloud-platformsAdvanced security implementation for AI systems in the cloud. Learn zero-trust principles, model protection, data encryption throughout ML pipeline, and compliance automation for regulated industries.
Dr. Aisha Patelfoundation-modelsLearn the fundamentals of supervised learning by implementing linear regression from scratch without libraries. Understand gradient descent, loss functions, and the mathematics behind ML.
AI Editorial Teamdata-engineeringMove beyond theory to practical feature engineering techniques that improve model performance in real-world applications. Learn which methods deliver ROI and which are time-wasters.
Sarah Chen