Building an E-commerce Recommendation System with MLOps: An Overview and Architecture

Modern e-commerce success relies heavily on personalized recommendations. When implemented effectively, recommendation systems can significantly boost user engagement, increase average order value, and drive sales.

Building Robust eCommerce Recommendation System

We're creating a robust e-commerce recommendation system for a platform like Meesho, which helps users discover products they are likely to purchase. The recommendation system will leverage machine learning to predict the most relevant items for a customer based on their past behavior, preferences, and interactions with the platform. By implementing MLOps practices, we'll ensure our model remains effective over time through:

• Security 
• Continuous monitoring 
• Version control 
• Scalable infrastructure 

The Problem We're Solving

E-commerce platforms face several challenges that our recommendation system addresses:

• Information Overload: Users struggle to find relevant products among thousands of options 
• Personalization at Scale: Each user has unique preferences that must be catered to 
• Real-Time Requirements: Recommendations need to update based on current browsing behavior 
• Evolving Preferences: User interests change over time, requiring model adaptability 

Project Focus - Building Machine Learning Model

The project will focus on building a machine learning model that can accurately predict what products a user will be interested in, helping the platform increase engagement and sales.

This architecture diagram illustrates an AWS-based MLOps (Machine Learning Operations) pipeline that handles data processing, storage, and machine learning workflows. Here's a breakdown of the components and their relationships:

• Data Flow Begins: The pipeline starts with a Data Set (shown as a folder with data chips) that feeds into a Source S3 bucket for initial storage. 
• ETL Process: From the Source S3 bucket, data flows to AWS Glue Databrew, which is a visual data preparation tool that helps clean and normalize data without coding. 
• Data Storage: After processing in Glue Databrew, the transformed data is stored in a Destination S3 bucket. 
• Data Cataloging: Simultaneously, data from Source S3 is also processed by AWS Glue Crawler, which automatically discovers and catalogs metadata from the data source. 
• Data Analysis: The cataloged data moves to Amazon Athena, which allows for SQL queries against data stored in S3. 
• Machine Learning: The query results from Athena flow to Amazon SageMaker, which is AWS's fully managed machine learning service for building, training, and deploying ML models. 
• Monitoring: Amazon SageMaker connects to Amazon CloudWatch for monitoring the performance of the ML models and the overall pipeline. 
• Visualization: Finally, SageMaker connects to Amazon QuickSight for business intelligence and visualization of the insights generated from the ML models.

How to Build E-commerce Recommendation System with MLOps

1. Data Source For Creting Robust Recommendation System

We're using the Meesho Recommendation System dataset from Kaggle, which contains:

• User Data: Unique user IDs  Demographic information (age, gender, location) 
• Product Data: Product IDs and URLs, categories and pricing information 
• Interaction Data: Purchase history and user ratings 

2. Data Preprocessing

Before training the model, several data preprocessing steps will be performed:

• Cleaning: Removing missing or irrelevant data.
• Feature Engineering: Creating features such as "recently viewed products" or "purchased together" to enhance recommendations.
• Normalization: Standardizing features like price to ensure they contribute equally to the model.
• Encoding: Encoding categorical data, such as product category and user demographics, for machine learning algorithms.

3. Data Storage and Management

While our implementation uses AWS S3 as the primary storage solution, below are the steps which shows the process of creating S3 bucket and storing the dataset into it:

Other than AWS S3 as storage we can also use following approach for data storage:

 1. Database Storage (Structured Data)

• SQL Databases (e.g., PostgreSQL): Store user data, transaction history, and product data in a relational database that can easily be queried.
• NoSQL Databases (e.g., MongoDB): Store semi-structured or unstructured data like user interactions and logs.

2. Cloud Storage (Unstructured Data):

• Amazon S3 / Google Cloud Storage: For storing large datasets, raw logs, and model files.
• Data Lakes: Store large volumes of unstructured data in a format that can be processed later.

3. Data Warehousing (Analytics):

• Amazon Redshift / Google BigQuery: For aggregating large amounts of data and running analytical queries to derive insights for model training.

4. Real-Time Data Streaming (For Real-Time Recommendations):

• Apache Kafka / AWS Kinesis: To stream user activity data (clicks, views, purchases) in real-time, ensuring that recommendations are updated as soon as new data arrives.

Conclusion

Building an effective e-commerce recommendation system requires both machine learning expertise and operational excellence. By implementing MLOps practices, we ensure our recommendation system stays current, performs reliably, and scales efficiently.

In upcoming posts, we'll dive deeper into data cleaning process, model training techniques, monitoring best practices, versioning, and security considerations for production recommendation systems.

If you are an eCommerce company looking to boost sales with generative AI-driven personalized recommendations system,

Book Your Free 45-minute Consultation with Our Generative AI Experts Today!