MandM Direct: Managing Models at Scale with Dataiku + GCP
Customer Company Size
Large Corporate
Region
- Europe
Country
- United Kingdom
Product
- Dataiku
- Google Cloud Platform (GCP)
- Google BigQuery
Tech Stack
- Python
- Machine Learning
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Productivity Improvements
- Digital Expertise
Technology Category
- Analytics & Modeling - Machine Learning
- Platform as a Service (PaaS) - Data Management Platforms
Applicable Industries
- Retail
Applicable Functions
- Sales & Marketing
- Business Operation
Use Cases
- Predictive Maintenance
- Inventory Management
- Supply Chain Visibility
Services
- Cloud Planning, Design & Implementation Services
- Data Science Services
About The Customer
MandM Direct is one of the largest online retailers in the United Kingdom with over 3.5 million active customers and seven dedicated local market websites across Europe. The company delivers more than 300 brands annually to 25+ countries worldwide. In 2020, they experienced rapid growth which resulted in an increase in customers and data. This growth magnified some of their challenges and pushed them to find more scalable solutions. The core data team is made up of four people (two data scientists, one senior analyst, and one data analyst), but they extend their reach by leveraging a hub and spoke model for their data center of excellence, meaning they work with analysts embedded across the business lines to scale their efforts.
The Challenge
MandM Direct, one of the largest online retailers in the United Kingdom, faced a significant challenge as they grew rapidly. With over 3.5 million active customers and seven dedicated local market websites across Europe, the company delivers more than 300 brands annually to 25+ countries worldwide. Their accelerated growth meant more customers and, therefore more data, which magnified some of their challenges and pushed them to find more scalable solutions. The two main challenges were getting all the available data out of silos and into a unified, analytics-ready environment and scaling out AI deployment in a traceable, transparent, and collaborative manner. Initially, the company's first machine learning models were written in Python (.py files) and run on the data scientist’s local machine. However, as the number of models in production increased, the team quickly realized the burden involved in maintaining models.
The Solution
To tackle their challenges, MandM Direct turned to the powerful combination of Dataiku and Google Cloud Platform (GCP). With Google BigQuery’s fully-managed, serverless data warehouse, MandM could break the data silos and democratize data access across teams. At the same time, thanks to Dataiku’s visual and collaborative interface for data pipelining, data preparation, model training, and MLOps, MandM could also easily scale out their models in production without failure or interruptions in a transparent and traceable way. MandM now has hundreds of live models doing everything from scoring customer propensity to generating pricing models, all with visibility into model performance metrics, clear separation of design and production environments, and many more MLOps capabilities built into the platform. Teams can now easily push-down and offload computations for both data preparation and machine learning to GCP.
Operational Impact
Quantitative Benefit
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