Optimizing Machine Learning Workflows: Integrating Comet with Amazon SageMaker AI
Navigating Enterprise ML Complexity with Comet and SageMaker AI
Empowering Machine Learning Teams Through Effective Experiment Management
A Step-by-Step Guide: Integrating Comet into Your ML Projects
User Journeys: From Setup to Experimentation in SageMaker AI
Enhancing Fraud Detection Workflows with Comet
Benefits of the Comet and SageMaker AI Partnership
Conclusion: Elevating Your ML Practices with Comprehensive Management Tools
About the Authors: Insights from Industry Experts
Optimizing Machine Learning in Enterprises: A Partnership Between Comet and Amazon SageMaker AI
This post was written with Sarah Ostermeier from Comet.
As enterprise organizations scale their machine learning (ML) initiatives from proof of concept to production, they encounter growing complexities in managing experiments, tracking model lineage, and ensuring reproducibility. These challenges intensify as data scientists and ML engineers explore myriad combinations of hyperparameters, model architectures, and dataset versions, generating massive amounts of metadata that must be meticulously tracked. The necessity for robust experiment tracking becomes even more pronounced in light of heightened regulatory requirements, particularly in the EU, where organizations must provide detailed audit trails of model training processes.
The Need for Experiments Management
Amazon SageMaker AI offers managed infrastructure to help enterprises scale their ML workloads, including compute provisioning, distributed training, and deployment. However, the need for advanced experiment tracking, model comparison, and collaboration capabilities remains essential. That’s where Comet comes into play.
Comet: A Comprehensive ML Experiment Management Platform
Comet is an all-in-one ML experiment management platform that automatically tracks, compares, and optimizes ML experiments throughout the model lifecycle. It offers powerful tools for experiment tracking, model monitoring, hyperparameter optimization, and collaborative model development tools, including Opik—an open-source platform for LLM observability.
Integrated as a fully managed experiment management capability within SageMaker AI, Comet provides seamless workflow integration, enterprise-grade security, and straightforward procurement via the AWS Marketplace, addressing the complex needs of enterprise ML workflows.
Setting Up Comet on SageMaker AI
Identifying the Operating Model
Before diving into setup instructions, organizations should identify their operating model. We recommend adopting a federated operating model, where Comet is centrally managed in a shared services account while allowing data science teams to operate autonomously. This architecture balances the benefits and limitations associated with different operating models.
Administrator Journey
Consider this scenario: an administrator is tasked with provisioning an ML environment for a fraud detection use case. The steps include:
- Prerequisites: Set up Partner AI Apps to establish permissions for administrators and allow Comet to assume a SageMaker AI execution role for users.
- SageMaker AI Console: Navigate to Partner AI Apps and choose Comet for details, including contract pricing and infrastructure estimates.
- Subscription: Select Go to Marketplace to subscribe and complete the necessary details.
- Configuration: After the subscription, configure Comet and add team project leads as admins.
- Domain Setup: Create a SageMaker AI domain and provide pre-signed URLs for seamless access to Comet.
User Journey
Once the admin has set up the necessary environment, ML practitioners log in through the provided URL and access the integrated SageMaker Studio IDE. They create JupyterLab spaces to kickstart their projects on the fraud detection use case and install the comet_ml library for API access.
Implementation Overview
This blog post highlights a common enterprise challenge—working with imbalanced datasets—such as in our fraud detection use case, where only 0.17% of transactions are fraudulent.
Prerequisites and Dataset Preparation
Preparation starts with necessary imports and environment variables that connect Comet and SageMaker.
import comet_ml
from comet_ml import Experiment, Artifact
from comet_ml.integration.sagemaker import log_sagemaker_training_job_v1
# API and workspace details
COMET_API_KEY = ""
COMET_WORKSPACE = ""
COMET_PROJECT_NAME = ""Next, create a Comet artifact to track the raw dataset, which ensures full auditability for regulatory compliance.
Tracking Experiments with Comet
Creating a Comet experiment begins the automatic logging of relevant data, such as hyperparameters, metrics, and system information, essential for reproducibility:
experiment_1 = comet_ml.Experiment(
project_name=COMET_PROJECT_NAME,
workspace=COMET_WORKSPACE,
)Data Preprocessing
Use SageMaker’s processing capabilities to preprocess your dataset efficiently, ensuring artifacts are versioned for complete lineage tracking. Following preprocessing, log the dataset to your experiment.
preprocessed_dataset_artifact = Artifact(
name="fraud-dataset",
artifact_type="dataset",
aliases=["preprocessed"],
)
# Add processed data
preprocessed_dataset_artifact.add_remote(
uri=f's3://{bucket_name}/{processed_data_prefix}',
)
experiment_1.log_artifact(preprocessed_dataset_artifact)Experiment Workflow & Utility Functions
Organize your experiments into reusable utility functions. This allows variations in hyperparameters while retaining consistent logging.
# Create a SageMaker estimator
estimator = Estimator(
image_uri=xgboost_image,
role=execution_role,
instance_count=1,
instance_type="ml.m5.large",
)Experiment Management and UI Tracking
Once experiments are complete, navigate to the Comet UI to compare results and visualize outcomes. The combination of both platforms allows for streamlined model development, enhanced collaboration, and full lifecycle integration.
Conclusion
This post demonstrated how to effectively utilize Amazon SageMaker and Comet together to create a fully managed ML environment, emphasizing the importance of reproducibility and comprehensive experiment tracking.
To enhance your own workflows, deploy Comet directly in your SageMaker environment through the AWS Marketplace, and share your experiences in the comments!
About the Authors
- Sarah Ostermeier: Technical Product Marketing Manager at Comet, specializing in ML products.
- Vikesh Pandey: Principal GenAI/ML Specialist Solutions Architect at AWS.
- Naufal Mir: Senior GenAI/ML Specialist Solutions Architect at AWS.
For more on SageMaker and Comet, check our additional resources and API documentation!