The Solution: Dataiku + GCP
MandM turned to the powerful combination of Dataiku and GCP to answer their two critical yet unique challenges. With Google BigQuery’s fully-managed, serverless data warehouse, MandM could break the data silos and democratize data access across teams. MandM Direct was one of the first online retailers to implement Google BigQuery across the organization.
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, 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. Using Dataiku means this capability is accessible to all user profiles across MandM, without knowing the underlying technologies or complexity.
Results, Impact, and What’s Next
The benefits MandM have seen by using Dataiku and GCP aren’t limited to time saved from tedious maintenance work — they are also having more impact across the business. The data team is now able to deliver a variety of business solutions on business problems from adtech to customer lifetime value, whether that’s a dashboard, a more detailed piece of analysis or a machine learning project deployed in production.
“Broadly, we love Dataiku. We do have a mix of people that go more toward AutoML and visual tools as well as one data scientist who loves to work in code. But that’s the beauty of Dataiku and why we chose it — we didn’t want a low-code tool where we could get lazy and just click a few buttons. Now the team has the flexibility: if they want to nerd out and go under the hood, they can do that. If they need a quick model, they can do that too.”
— Ben Powis, Head of Data Science at MandM Direct
For example, one application might be business users in the buying and merchandising teams, who could interact with machine learning models in their day-to-day work through Dataiku applications, which provide a nontechnical interface for projects developed by the data team.
The team is also particularly proud of the work they’ve done to build out a feature library with Dataiku that contains more than 400 features specific to MandM’s business. Now, the feature library is the first place people go, sort of like a shop window for machine learning projects — it takes away the monotony and repetition of their work.