Microsoft Manufacturing Japan

Sony Semiconductor Solutions Corporation

Application Refactoring · Real-time Analytics

One technology people expect to see everywhere in the near future is automated driving systems in automobiles. As these systems grow in sophistication, we can expect them to spawn all kinds of solutions to social problems. They can be expected to solve the shortage of drivers, for example, prevent traffic accidents, and give rise to new ways of passing the time during journeys (“secondary activities”).

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Igarashi recalls, “Our department began work on software development in 2016.

Reported impact

Business outcome

While we appreciate that using cloud tools to build the infrastructure has improved the efficiency of our development work, we must never forget that these are only tools,” cautions Igarashi. The people who use these tools must keep their eyes on the goal, maintaining full and open communication to ensure that we continue to use only the necessary methods,” adds Igarashi, as Haruta nods in enthusiastic agreement.

Solution

Sony Semiconductor Solutions Corporation built this solution on Azure AI Studio, Azure Machine Learning. One technology people expect to see everywhere in the near future is automated driving systems in automobiles. As these systems grow in sophistication, we can expect them to spawn all kinds of solutions to social problems. Azure AI Studio supports building, testing, and validating AI workflows and prompts for the solution. Azure Machine Learning supports model training, evaluation, and deployment for machine learning workloads. AVOps streamlines data ingestion, annotation and machine learning workflows, accelerating the development of sensor fusion systems and improving recognition performance. An image scene analyzing system using Azure AI Studio is now being validated.

Solution architecture

2 components · 1 layer

Compute
- Azure AI Studio Supports building, testing, and validating AI workflows and prompts for the solution.
- Azure Machine Learning Supports model training, evaluation, and deployment for machine learning workloads.

Architecture clues

The implementation relies on Azure AI Studio as a core technical building block.

Evidence from the source

AVOps streamlines data ingestion, annotation and machine learning workflows, accelerating the development of sensor fusion systems and improving recognition performance.

Igarashi recalls, “Our department began work on software development in 2016.

One technology people expect to see everywhere in the near future is automated driving systems in automobiles.

While we appreciate that using cloud tools to build the infrastructure has improved the efficiency of our development work, we must never forget that these are only tools,” cautions Igarashi.

Transparency Evidence & methodology 60/100 reference value · 85% confidence · 1 source

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 value60/100

Problem clarity20/20
Measures whether the customer's operational problem is specific enough to compare.
Implementation depth15/25
Rewards named components, data flow, integrations, and architecture detail.
Outcome specificity20/20
Measures whether the reported benefit is concrete rather than a generic claim.
Quantified impact0/25
0 metrics describe measurable impact.
Deployment scale5/10
Rewards evidence of a live or scaled deployment and meaningful integration scope.

Evidence confidence85%

Source authority20/25
The main evidence is an official vendor-published customer story.
Claim traceability21/25
10 of 12 key fields link to a named source.
Direct evidence20/20
4 direct source quotes retained.
Date provenance10/10
The publication date comes from the source page or publisher.
Source transparency8/10
1 source reference document publisher, URL, authority, retrieval, and supported fields.
Implementation specificity6/10
Rewards 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.

Microsoft
Sony Semiconductor Solutions Corporation customer story

First-party
https://www.microsoft.com/en/customers/story/19179-sony-semiconductor-solutions-corporation-azure-ai-studio

Source type
Vendor-published customer story

Published
Jan 7, 2025
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 Jan 7, 2025 · Microsoft · First-party
Open 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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