ZenML vs AWS SageMaker: Supercharge Your ML Workflows

Unlock the full potential of your machine learning projects with ZenML, a flexible alternative to AWS SageMaker. While SageMaker offers a comprehensive cloud platform for ML, ZenML provides a vendor-neutral approach to building, training, and deploying high-quality models at scale. ZenML's intuitive workflow management capabilities extend beyond a single cloud provider, offering the flexibility to work across various environments and tools. Unlike SageMaker's AWS-centric ecosystem, ZenML allows you to accelerate your time-to-market and drive innovation across your organization without being locked into a specific cloud infrastructure, giving you the freedom to adapt your ML workflows as your needs evolve.

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Enhanced Workflow Portability

ZenML enables you to develop and test your ML workflows locally before seamlessly deploying them to SageMaker.
With ZenML, you can easily switch between different orchestrators, including SageMaker, without modifying your pipeline code.
This flexibility allows you to avoid vendor lock-in and ensures your workflows remain portable across different environments.

Streamlined MLOps Lifecycle Management

ZenML provides a unified platform for managing the entire MLOps lifecycle, from data preparation to model monitoring.
By integrating SageMaker with ZenML, you can leverage SageMaker's powerful features while benefiting from ZenML's end-to-end workflow orchestration.
ZenML's intuitive interface and pre-built integrations simplify the management of SageMaker resources, reducing complexity and increasing efficiency.

No Lock-In and Easy Migration

With ZenML, you can continue using SageMaker for your ML workflows while keeping your options open for future changes.
If you decide to switch to a different orchestrator or cloud provider, ZenML makes it easy by allowing you to simply switch out your stack without modifying your pipeline code.
This flexibility ensures that you can adapt to changing business requirements and take advantage of new technologies without being locked into a single vendor or platform.

ZenML allowed us a fast transition between dev to prod. It’s no longer the big fish eating the small fish – it’s the fast fish eating the slow fish.

François Serra

ML Engineer / ML Ops / ML Solution architect at ADEO Services

Feature-by-feature comparison

Explore in Detail What Makes ZenML Unique

Feature

ZenML

AWS Sagemaker

Workflow Orchestration	Provides a flexible and portable orchestration layer for ML workflows	Offers orchestration capabilities within the SageMaker ecosystem
Integration Flexibility	Seamlessly integrates SageMaker with other MLOps tools for a customized stack	Primarily focuses on integration within the AWS ecosystem
Vendor Lock-In	Enables easy migration between orchestrators and cloud providers	Tight coupling with AWS services may lead to vendor lock-in
Local Development	Supports local development and testing of ML workflows before deployment	Limited local development capabilities, primarily cloud-based
MLOps Lifecycle Coverage	Covers the entire MLOps lifecycle, from data preparation to model monitoring	Covers the entire MLOps lifecycle. Some parts are more integrated than others.
Collaborative Development	Facilitates collaboration among teams with version control and governance features	Provides collaboration features through SageMaker Studio
Portability	Ensures workflow portability across different environments and platforms	Primarily optimized for the AWS environment
Experiment Tracking	Integrates with MLflow and other tools for comprehensive experiment tracking	Offers SageMaker Experiments for experiment tracking
Model Deployment	Simplifies model deployment across various platforms, including SageMaker	Supports deployment within the SageMaker ecosystem
Monitoring and Logging	Provides centralized monitoring and logging for ML workflows	Offers monitoring and logging capabilities through AWS services
Community and Support	Growing community with active support and resources	Large community and extensive support through AWS
Pricing Model	Flexible pricing model based on usage and scale	Pay-as-you-go pricing model tied to AWS service usage
Learning Curve	Reduces the learning curve by providing a consistent interface across platforms	Requires familiarity with AWS services and SageMaker concepts
Hybrid and Multi-Cloud	Supports hybrid and multi-cloud deployments with easy migration	Primarily optimized for AWS, with limited multi-cloud support

Code comparison

ZenML and

AWS Sagemaker

side by side

ZenML


# ZenML with SageMaker integration
from zenml import pipeline, step
from zenml.integrations.aws.flavors import SagemakerOrchestratorFlavor
from zenml.integrations.aws.sagemaker_orchestrator_settings import SagemakerOrchestratorSettings

# Set up SageMaker orchestrator
sagemaker_orchestrator = SagemakerOrchestratorFlavor(
    execution_role="arn:aws:iam::123456789012:role/SageMakerRole",
    # Other configuration options...
)

# Define custom settings for specific steps
gpu_settings = SagemakerOrchestratorSettings(
    processor_args={
        "instance_type": "ml.p3.2xlarge",
        "volume_size_in_gb": 100
    },
    input_data_s3_uri="s3://your-bucket/training-data"
)

@step
def prepare_data():
    # Data preparation logic
    return processed_data

@step(settings={"orchestrator.sagemaker": gpu_settings})
def train_model(data):
    # Training logic using SageMaker
    return model

@step
def evaluate_model(model):
    # Model evaluation logic
    return metrics

@pipeline(orchestrator=sagemaker_orchestrator)
def sagemaker_pipeline():
    data = prepare_data()
    model = train_model(data)
    metrics = evaluate_model(model)

# Run the pipeline
sagemaker_pipeline()

AWS Sagemaker


import boto3
import sagemaker
from sagemaker.estimator import Estimator
from sagemaker.inputs import TrainingInput

# Set up SageMaker session
sagemaker_session = sagemaker.Session()
role = sagemaker.get_execution_role()

# Define estimator
estimator = Estimator(
    image_uri="your-docker-image-uri",
    role=role,
    instance_count=1,
    instance_type="ml.m5.xlarge",
    output_path="s3://your-bucket/output"
)

# Set hyperparameters
estimator.set_hyperparameters(epochs=10, learning_rate=0.1)

# Prepare data
train_data = TrainingInput(
    s3_data="s3://your-bucket/train",
    content_type="text/csv"
)

# Train the model
estimator.fit({"train": train_data})

# Deploy the model
predictor = estimator.deploy(
    initial_instance_count=1,
    instance_type="ml.t2.medium"
)

# Make predictions
result = predictor.predict("sample input data")
print(result)