Learn how we guided a German manufacturing leader throughout their AI journey by assessing their maturity and implement a Data Science Platform, standardizing an agile project management and establishing data governance processes.
Context & Key challenges: From Experimentation to Operationalization
Our client is a leading German manufacturer of white goods, recognized for high-quality products across their entire portfolio. When we first met, they had just moved their first use case from experimentation to operationalization by putting their very first use case into production.
However, their approach had significant gaps:
- Fragmented operations: More than 10 data science labs working on similar use cases with completely different methods
- Manual processes: Very artisanal approach to managing machine learning model lifecycles
- Siloed organization: Decentralized Data Science teams using different tools and approaches
They needed our expertise to:
- Challenge their current approach and lab initiatives
- Structure and operationalize model development processes
- Improve their go-to-market approach and effectiveness of their data products
Our Approach: AI Maturity Assessment and Centralized Platform Implementation
We addressed their challenges through a comprehensive two-phase approach:
Phase 1: AI maturity assessment
Assess the company AI Maturity Level and the organization’s infrastructure and processes dimensions in detail. Among others, we discovered that more than 10 data science labs were working on similar use cases in ten different ways.
Phase 2: Strategic implementation
Based on this comprehensive analysis, we provided a set of recommendations to enable our client to operationalize their initiative. Among them, we strongly insisted on the need to create a competence hub including IT, Business and Data Science teams. Their role is to manage a centralized Data Science Platform but also provide guidance and processes for developing and deploying ML models.
Based on our findings and with a clear AI strategy in mind, our client decided to extend our support to include:
- Data Science Platform Creation: Built a centralized platform in collaboration with a newly established Hub of Excellence and by implementing MLOps best practices
- Agile Project Management: Standardized project management structure ensuring its adoption using Jira and covering the entire journey from Proof of Concept to MVP to Production
- Governance Framework: Established comprehensive processes for data product lifecycle management
- Use Case Deployment: Implemented and deployed the first production-ready use cases
Benefits: Centralized AI Operations and Audit-Proof Model Deployment
- Clear AI strategy with standardized processes and best practices
- Increased efficiency while establishing innovative methods to become a data-driven company.
- Centralized Data Science Platform with consolidated know-how, new roles (Data Architect), and governance frameworks
- Transparent project controlling using agile project management frameworks
- Audit-proof operationalization of machine learning models and data products
Technologies Used
The project implementation brought a comprehensive technology ecosystem designed for scalable data processing, machine learning operations, and collaborative development. Each technology was strategically selected to create an integrated platform capable of handling complex data workflows and advanced analytics requirements.
- Databricks: Its integrated environment enabled seamless collaboration between data scientists and engineers while supporting large-scale data processing workflows.
- Python: Its versatility enabled rapid prototyping, statistical analysis, and integration with various data processing tools throughout the technology stack.
- Apache Spark: Spark’s in-memory processing architecture significantly accelerated data transformation, feature engineering, and machine learning model training operations, ensuring optimal performance for compute-intensive workloads.
- Azure Machine Learning: The platform’s integrated tools supported the entire machine learning lifecycle from experimentation to production deployment, ensuring scalable and maintainable AI solutions.
- MLflow: This open-source platform ensured consistent model governance, simplified collaboration between team members, and streamlined the transition from development to production environments.
- Atlassian: The integrated toolset facilitated efficient workflow management, code review processes, and documentation throughout the project lifecycle.
