Learn how Shopify Data built new online inference capabilities into its Machine Learning Platform to deploy and serve models for real-time prediction at scale.
Overview
The article discusses Shopify's Merlin machine learning platform, focusing on its online inference capabilities for real-time predictions. It details the architecture, features, and deployment process of Merlin, emphasizing the importance of low latency and scalability in serving machine learning models.
What You'll Learn
1
How to deploy machine learning models for real-time predictions using Merlin
2
Why low latency is critical for user-facing machine learning applications
3
How to create a serving layer for machine learning models with MLServer
4
When to utilize different serving layer types in Merlin
Prerequisites & Requirements
- Understanding of machine learning concepts and workflows
- Familiarity with Kubernetes and Docker(optional)
Key Questions Answered
What is online inference and how does it differ from batch inference?
Online inference allows for real-time predictions as input data becomes available, in contrast to batch inference, which processes data periodically. This capability is crucial for applications like recommender systems and fraud detection, where immediate responses are necessary.
What are the key features of Merlin's online inference capabilities?
Merlin's online inference features include robust model serving, low latency, state-of-the-art deployment options like rolling updates and autoscaling, and integration with the Merlin platform for model registry and feature access.
How does the serving layer in Merlin work?
The serving layer in Merlin acts as an API endpoint that processes client inputs and returns predictions. It initializes by loading the model and artifacts into memory, and it supports different serving libraries like MLServer and FastAPI.
What is the process for deploying a Merlin service?
To deploy a Merlin service, users create a service using the Merlin CLI, which integrates with Shopify's infrastructure. The service is registered in the Services DB and is automatically built and deployed using CI/CD pipelines on Shopify’s Kubernetes clusters.
Technologies & Tools
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Machine Learning Platform
Merlin
Used for deploying and serving machine learning models.
Inference Server
Mlserver
Provides a no-code and low-code solution for serving machine learning models.
Web Framework
Fastapi
Used for building APIs to serve machine learning models.
Container Orchestration
Kubernetes
Used for deploying Merlin services in a scalable manner.
Containerization
Docker
Used to create images for Merlin services.
Experiment Tracking
Comet ML
Used for model registry and experiment tracking.
Feature Store
Feast
Used for managing features in machine learning workflows.
Key Actionable Insights
1Utilize Merlin's online inference capabilities to enhance user experience by providing real-time predictions.Real-time predictions can significantly improve user engagement and satisfaction, especially in applications like product recommendations and fraud detection.
2Leverage the flexible serving layer options in Merlin to meet specific use-case requirements.By choosing between no-code, low-code, and fully customizable serving layers, teams can optimize their deployment process based on their technical expertise and project needs.
3Implement monitoring dashboards for your Merlin services to track performance metrics.Monitoring is essential to ensure that your machine learning models are performing optimally and to quickly identify any issues that may arise during inference.
Common Pitfalls
1
Failing to account for latency in online inference can lead to poor user experiences.
Latency is critical for real-time applications; if not properly managed, it can result in slow response times that frustrate users and impact business performance.
2
Overcomplicating the serving layer can hinder deployment efficiency.
Choosing overly complex solutions when simpler options are available can slow down the deployment process and create unnecessary technical debt.
Related Concepts
Real-time Predictions
Machine Learning Model Deployment
Feature Stores
Model Registry