Case study: FMCG, MLOps

ML Workflow Standardization: One MLOps Package Became the Company Standard

DS Stream delivered ML workflow standardization for a global FMCG / CPG company whose machine learning work was fragmented across teams and clouds. We built a cloud-agnostic MLOps package for ML pipelines on GCP Vertex AI, Azure ML and Databricks. It became the organization's standard deployment tool and lowered costs and reliance on specialized expertise.

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Key results

The MLOps package quickly became the standard tool for ML deployment across the organization.
Costs fell through leaner infrastructure requirements, lower project startup expenses and less reliance on specialized expertise.
Business and ML teams aligned better, which led to faster responses, better outcomes and more organizational trust.
A transparent, standardized approach let technical and non-technical stakeholders engage confidently in ML initiatives.

What slowed ML deployment at this global FMCG company?

ML demand was rising, but the machine learning landscape was fragmented across many teams. Without a standard, every project needed deep engineering and DevOps expertise. High initial costs, several cloud environments and the need for consistent engineering standards made ML deployment cumbersome and held back scalability and innovation.

Main pain points

Fragmented ML work spread across multiple teams
No shared standard, so each project carried extra complexity
Each project required both engineering and DevOps depth
High initial costs for every new ML project
Multiple cloud environments to support at once

What did DS Stream build to standardize ML pipelines?

DS Stream built a comprehensive MLOps package for creating ML pipelines. It works as an abstraction layer that simplifies ML system deployment across the company. The package is cloud-agnostic, fully customizable and targets GCP Vertex AI, Azure ML and Databricks, so teams follow one approach on any of these platforms.

Design principles

Cloud-agnostic: one framework for GCP Vertex AI, Azure ML and Databricks
Infrastructure as code: environments defined and reproduced in code
CI/CD integration: works with GitHub Actions and existing CI/CD pipelines
Standard but adaptable: a consistent structure that can still be customized to specific project needs
Transparent: an intuitive framework that makes high engineering standards accessible to all teams

The work fits within our machine learning pipeline and MLOps implementation services.

How did ML workflow automation change the way teams work?

The package was adopted fast and became the standard for ML deployment. Business and ML teams backed it for its seamless integration and adherence to best practices. Teams now follow one unified ML workflow, operations are streamlined, and the company stays aligned with state-of-the-art MLOps trends.

Business impact

Lower costs from streamlined infrastructure and cheaper project startup
Less dependence on scarce specialized expertise
Faster responses and better outcomes through closer business and ML alignment
Better communication and reliability thanks to a transparent, standardized approach
A culture of collaboration and consistency across the company

Before and after the MLOps package

Qualitative comparison based on the client situation described in this case study.
Area	Before	After
ML landscape	Fragmented across multiple teams	Unified approach to ML workflows
Required expertise	Deep engineering and DevOps skills for each project	High engineering standards accessible to all teams
Project startup	High initial costs	Lower project startup expenses
Cloud environments	Multiple clouds handled separately	One cloud-agnostic package for GCP Vertex AI, Azure ML and Databricks
Team alignment	Complexity hindered scalability and innovation	Business and ML teams aligned, with faster responses and more trust

FAQ about this MLOps case study

Which platforms does the MLOps package support?

The package is cloud-agnostic and fully customizable. In this project it targeted GCP Vertex AI, Azure ML and Databricks, which let a global FMCG company with several cloud environments deploy ML pipelines through one standardized framework instead of separate approaches per cloud.

How does the package fit into existing engineering practices?

It follows infrastructure-as-code principles and integrates with GitHub Actions and CI/CD pipelines. Teams keep their delivery workflow, while the package supplies a consistent, transparent structure for building and deploying ML pipelines across the organization.

Do teams still need deep DevOps expertise to deploy ML models?

Less than before. The package acts as an abstraction layer over ML system deployment, so high engineering standards are built in. This reduced the company's reliance on specialized expertise and made consistent deployment accessible to all teams.