The Magic of Merlin: Shopify's New Machine Learning Platform

Merlin, Shopify’s machine learning platform that can handle different (often conflicting) requirements, inputs, data types, dependencies, and integrations.

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Shopify
•
Isaac Vidas
•14 min read•intermediate•
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•View Original
DatadogDockerKubernetesMachine LearningPyTorchscikit-learnSplunkTensorFlowXGBoostYAML

Overview

Shopify's new machine learning platform, Merlin, is designed to enhance the efficiency of data scientists by providing a robust infrastructure and tools for machine learning workflows. The platform supports various use cases, including fraud detection, product categorization, and recommendation systems, while leveraging open-source technologies like Ray for distributed computing.

What You'll Learn

1

How to create and manage a Merlin Project for machine learning tasks

2

Why using Ray enhances distributed machine learning workflows

3

How to prototype machine learning models using Jupyter Notebooks in Merlin

4

How to automate machine learning workflows using Airflow with Merlin

Prerequisites & Requirements

  • Familiarity with machine learning concepts and workflows
  • Basic understanding of Docker and Kubernetes(optional)
  • Experience with Python programming

Key Questions Answered

What is the purpose of Shopify's Merlin machine learning platform?
Merlin is designed to streamline, accelerate, and simplify machine learning workflows for data scientists at Shopify. It provides the necessary infrastructure and tools to train, test, deploy, serve, and monitor machine learning models efficiently, catering to both internal and external use cases.
How does Ray contribute to the functionality of Merlin?
Ray provides a simple API for building distributed systems and parallelizing machine learning workflows. In Merlin, Ray is used for distributed preprocessing, training, and prediction, allowing data scientists to scale their computations with minimal code changes.
What are Merlin Workspaces and how do they function?
Merlin Workspaces are dedicated environments for running machine learning tasks, defined by their specific requirements and resources. They are built on Ray clusters deployed on Kubernetes, enabling scalability and distributed computing for various machine learning workflows.
What steps are involved in moving a project from prototyping to production in Merlin?
The process involves creating a Merlin Project, prototyping in a Merlin Workspace, updating the project with finalized code, and automating workflows using tools like Airflow. This structured approach ensures a smooth transition from development to production.

Technologies & Tools

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Backend
Ray
Used for building distributed systems and parallelizing machine learning workflows.
Infrastructure
Kubernetes
Used for deploying and managing Ray clusters in Merlin Workspaces.
Containerization
Docker
Used to create isolated environments for Merlin Projects.
Orchestration
Airflow
Used for scheduling and managing machine learning workflows in production.

Key Actionable Insights

1
Leverage Merlin's Workspaces to prototype machine learning models efficiently.
Using dedicated environments allows data scientists to experiment with different models and parameters without affecting the production environment, thus reducing the risk of errors during deployment.
2
Integrate Ray for distributed training to enhance model performance.
Ray's capabilities enable seamless scaling of machine learning tasks, allowing teams to handle larger datasets and complex models with minimal code modifications, which is crucial for maintaining competitive edge.
3
Utilize Airflow for orchestrating machine learning workflows in production.
By automating the scheduling and execution of machine learning jobs, teams can ensure consistency and reliability in their model deployments, leading to improved operational efficiency.

Common Pitfalls

1
Failing to properly configure resource requirements for Merlin Workspaces can lead to inefficient use of computational resources.
Without careful planning, users may either underutilize or overprovision resources, which can increase costs or slow down processing times. It's essential to assess the specific needs of each machine learning task before deployment.

Related Concepts

Distributed Machine Learning
Machine Learning Model Deployment
Cloud Infrastructure Management