Compare ZenML vs
AWS Sagemaker

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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ZenML
vs
AWS Sagemaker

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.
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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.
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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
ZenML
AWS Sagemaker
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

# 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
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)

Streamlined ML Workflow Initialization

ZenML guarantees swifter initialization, surpassing orchestrators for prompt, optimized ML workflows.

Supporting All Your Tools

ZenML is a native interface to the whole end-to-end machine learning lifecycle, taking you beyond just orchestration.

Unrivaled User Assistance

ZenML excels with dedicated support, offering personalized assistance beyond standard orchestrators.

Outperform Orchestrators: Book Your Free ZenML Strategy Talk

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Expand Your Knowledge

Broaden Your MLOps Understanding with ZenML

New Features: Performance Upgrade, Improvements for Major Cloud Providers, and More!

ZenML 0.74.0 introduces key cloud provider features including SageMaker pipeline scheduling, Azure Container Registry implicit authentication, and Vertex AI persistent resource support. The release adds API Tokens for secure, time-boxed API authentication while delivering comprehensive improvements to timezone handling, database performance, and Helm chart deployments.

From Chaos to Control: A Guide to Scaling MLOps Automation

Discover how organizations can transform their machine learning operations from manual, time-consuming processes into streamlined, automated workflows. This comprehensive guide explores common challenges in scaling MLOps, including infrastructure management, model deployment, and monitoring across different modalities. Learn practical strategies for implementing reproducible workflows, infrastructure abstraction, and comprehensive observability while maintaining security and compliance. Whether you're dealing with growing pains in ML operations or planning for future scale, this article provides actionable insights for building a robust, future-proof MLOps foundation.

AWS MLOps Made Easy: Integrating ZenML for Seamless Workflows

Machine Learning Operations (MLOps) is crucial in today's tech landscape, even with the rise of Large Language Models (LLMs). Implementing MLOps on AWS, leveraging services like SageMaker, ECR, S3, EC2, and EKS, can enhance productivity and streamline workflows. ZenML, an open-source MLOps framework, simplifies the integration and management of these services, enabling seamless transitions between AWS components. MLOps pipelines consist of Orchestrators, Artifact Stores, Container Registry, Model Deployers, and Step Operators. AWS offers a suite of managed services, such as ECR, S3, and EC2, but careful planning and configuration are required for a cohesive MLOps workflow.

Experience the ZenML Difference: Book Your Customized Demo

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