Reported impact
Business outcome
"Marks & Spencer has more than 30 million customers and large amounts of data that require systems that can scale to process it.
- Customer Experience
- Productivity Improvement
- Revenue Growth
- Scalability
MicrosoftRetailMarketing PersonalizationAI and Machine LearningData AnalyticsData PlatformDevOpsCustomer ExperienceProductivity ImprovementRevenue GrowthScalability2023 MicrosoftMS#0078d4RetailUnited KingdomThe previous solution for data science workloads relied on many manual processes, and Marks & Spencer wanted a faster, less labor-intensive way to get models into production."Marks & Spencer has more than 30 million customers and large amounts of data that require systems that can…Jul 18, 20236Marketing PersonalizationAzure Databricks · Azure DevOps · Azure Machine Learning0 2023-07-18 6
Marks & Spencer
Marketing Personalization
The previous solution for data science workloads relied on many manual processes, and Marks & Spencer wanted a faster, less labor-intensive way to get models into production.
"Marks & Spencer has more than 30 million customers and large amounts of data that require systems that can scale to process it.
By using ParallelRunStep, the migration achieved significant improvements in performance, reliability, and reduced execution time by 50 percent.
Its customer loyalty program, Sparks, has more than 17 million members in the UK and Ireland.
Solution
Marks & Spencer built its Microsoft solution to improve customer-personalization workflows. The previous solution for data science workloads relied on many manual processes, and Marks & Spencer wanted a faster, less labor-intensive way to get models into production. Key components included Azure Machine Learning ran model inference or heavy processing, and Azure Databricks ran model inference or heavy processing. Operational data flows into Azure Machine Learning, where Azure Machine Learning applies transformations and business logic before Azure Machine Learning delivers governed insights to teams across Marks & Spencer. The governed Microsoft stack gave Marks & Spencer a reusable foundation for ai and machine learning, data analytics while keeping deployment, security, and day-to-day operations inside the Microsoft estate. "Marks & Spencer has more than 30 million customers and large amounts of data that require systems that can scale to process it.
Data flow
Operational data flows into Azure Machine Learning, where Azure Machine Learning applies transformations and business logic before Azure Machine Learning delivers governed insights to teams across Marks & Spencer.
Solution architecture
- Compute
- Azure Machine Learning Builds, trains, and operationalizes predictive models and machine-learning pipelines.
- Azure Databricks Transforms large-scale datasets and runs analytics or model-processing jobs.
- Orchestration
- Azure DevOps Supports deployment, testing, and release management for the Microsoft solution estate.
Architecture clues
- Azure Databricks handles data processing, extraction, transformation, and aggregation, including accessing and writing data into and from Azure Data Lake Storage Gen 2 Azure Data Factory handles the orchestration of pipelines, the CI/CD pipeline is built on Azure DevOps , and Azure Machine Learning handles model training and optimization.
- Building a robust machine learning system Now, the MLOps team can seamlessly manage two Azure Machine Learning environments-one for development, another for production-and the CI/CD pipeline makes deployments directly.
- The CI/CD pipeline creates an efficient process that speeds up time to production through automated tests that help developers review code quality and perform bug fixes faster.
- The new templates were integrated into the continuous integration/continuous delivery (CI/CD) pipeline so that deployments can be made directly in Azure Machine Learning.
Evidence from the source
"Marks & Spencer has more than 30 million customers and large amounts of data that require systems that can scale to process it.
Azure Machine Learning allows us to build machine learning solutions that can scale and give customers the right offers and better service overall." Luis Arnedo Martinez, Machine Learning Platform Product Manager, Marks & Spencer Data-driven sales Behind the food, clothing, and homeware that Marks & Spencer supplies to millions of customers are thousands of employees committed to providing the best customer experiences.
By using ParallelRunStep, the migration achieved significant improvements in performance, reliability, and reduced execution time by 50 percent.
Its customer loyalty program, Sparks, has more than 17 million members in the UK and Ireland.
The previous solution for data science workloads relied on many manual processes, and Marks & Spencer wanted a faster, less labor-intensive way to get models into production.
Transparency Evidence & methodology
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
How scores are calculated
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Customer reference value91/100
- Problem clarity20/20Measures whether the customer's operational problem is specific enough to compare.
- Implementation depth20/25Rewards named components, data flow, integrations, and architecture detail.
- Outcome specificity16/20Measures whether the reported benefit is concrete rather than a generic claim.
- Quantified impact25/252 metrics describe measurable impact.
- Deployment scale10/10Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
- Source authority20/25The main evidence is an official vendor-published customer story.
- Claim traceability21/2510 of 12 key fields link to a named source.
- Direct evidence20/205 direct source quotes retained.
- Date provenance10/10The publication date comes from the source page or publisher.
- Source transparency8/101 source reference document publisher, URL, authority, retrieval, and supported fields.
- Implementation specificity10/10Rewards source-backed components, data flow, quantified impact, and a specific outcome.
Traceability
Sources & provenance
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
- https://www.microsoft.com/en/customers/story/1638626120995556543-marks-and-spencer-retailer-azure-machine-learningMicrosoftFirst-party
Marks & Spencer customer story
Supports- Customer
- Business problem
- Solution
- Products
- Solution components
- Data flow
- Integrations
- technical area
- use case category
- Business outcome
- outcome category
- Metrics
- Architecture
- evidence quotes
- Publication date
Date evidence
- Published on source page Jul 18, 2023 · Microsoft · First-partyOpen date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.
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