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In this blog, we’ll explore how to set up MLflow, track experiments, log models, and leverage AWS SageMaker for scalable machine learning workflows. The provided code snippet demonstrates a complete pipeline using Scikit-learn to train and track a Random Forest model in MLflow. Let’s dive into the details!
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Introduction to MLflow
MLflow is an open-source platform for managing the complete machine learning lifecycle, including:
- Experiment Tracking: Logging and querying experiments with parameters, metrics, and artifacts.
- Model Registry: Managing model lifecycle stages (staging, production).
- Deployment: Integrating models into various deployment environments.
By integrating MLflow with AWS SageMaker, we gain access to scalable infrastructure for training, deploying, and managing models.
Setting Up the Environment
To start, we need to configure our environment to use MLflow with SageMaker. We’ll also use S3 to store model artifacts.
AWS Prerequisites
- S3 Bucket: Create a bucket for storing MLflow artifacts (e.g., mlflow-sagemaker-artifacts-demo).
- IAM Role: Ensure you have an IAM role with necessary permissions for SageMaker and S3 (replace <your-account-id> in the role ARN).
Code Walkthrough
- Import Libraries and Set Up Resources
We begin by importing the required libraries and setting up the necessary AWS resources.
- Configure MLflow
You can also Set up MLflow to work with your environment as follows:
- MLFLOW_S3_ENDPOINT_URL specifies the endpoint for S3.
- mlflow.set_tracking_uri points to the MLflow tracking server. Replace http://localhost:5000 with the appropriate server URI if not running locally.
- Set Up an Experiment
Specify an experiment name. If the experiment doesn’t exist, MLflow will create it automatically.
- Start an MLflow Run and Train the Model
Within an active MLflow run:
- Log parameters and metrics: Track hyperparameters (n_estimators) and evaluation metrics (accuracy).
- Log the model: Save the trained model and register it.
Viewing Results in MLflow UI
- Start the MLflow server locally or in a hosted environment, With following commands on your command prompt:
- Open the MLflow UI: Navigate to http://localhost:5000. You should see:
- Experiment: “Demo Experiment”
- Runs: Logs of parameters, metrics, and the trained model.
Extending to SageMaker
To integrate this setup with SageMaker, follow these steps:
- Using SageMaker Studio
- Open SageMaker Studio, create a new notebook, and paste the code.
- SageMaker Studio automatically supports MLflow, and you can configure it to use your MLflow tracking server.
- SageMaker Model Deployment
After logging the model with MLflow, you can deploy it using SageMaker:
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mlflow.sagemaker.deploy( app_name="random-forest-app", model_uri="s3://mlflow-sagemaker-artifacts-demo/<model-path>", region_name=REGION, mode="create" ) |
Best Practices
- Use S3 for Artifacts: Store large model artifacts in S3 for better scalability.
- Secure Access: Use IAM roles to manage permissions for accessing S3 and SageMaker resources.
- Automate Deployment: Automate SageMaker model deployment with CI/CD pipelines.
- Extend the setup to SageMaker for scalable deployment.
Conclusion
This guide demonstrated how to:
- Set up MLflow for experiment tracking.
- Train and log a model.
- View results in the MLflow UI.
By combining MLflow and SageMaker, you can effectively manage the entire ML lifecycle, from experimentation to production deployment.
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About CloudThat
CloudThat is an award-winning company and the first in India to offer cloud training and consulting services worldwide. As a Microsoft Solutions Partner, AWS Advanced Tier Training Partner, and Google Cloud Platform Partner, CloudThat has empowered over 850,000 professionals through 600+ cloud certifications winning global recognition for its training excellence including 20 MCT Trainers in Microsoft’s Global Top 100 and an impressive 12 awards in the last 8 years. CloudThat specializes in Cloud Migration, Data Platforms, DevOps, IoT, and cutting-edge technologies like Gen AI & AI/ML. It has delivered over 500 consulting projects for 250+ organizations in 30+ countries as it continues to empower professionals and enterprises to thrive in the digital-first world.
WRITTEN BY Priya Kanere
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