Download PDF
---nasdaq--amzn_14.jpg "Amazon Web Services Logo")
Mirriad Delivers Next-Generation Ad Tech Using AWS
Technology Category
- Analytics & Modeling - Machine Learning
- Infrastructure as a Service (IaaS) - Cloud Computing
- Analytics & Modeling - Computer Vision Software
Applicable Industries
- Automotive
Applicable Functions
- Sales & Marketing
- Business Operation
Use Cases
- Computer Vision
- In-process Traceability
Services
- Cloud Planning, Design & Implementation Services
- Data Science Services
The Challenge
Mirriad, a London-based company, delivers next-generation advertising solutions by using its computer vision technology to naturally place brands in premium video content across TV, online, and mobile channels. The challenge for Mirriad was achieving this intelligent insertion of ads at scale. The company was initially using physical data centers, which proved to be a bottleneck when it came to onboarding big businesses. In addition to fast access to compute capacity, another vital requirement for the company was the ability to use NVIDIA GPUs across multiple regions worldwide.
About The Customer
Mirriad is a video technology company based in London that delivers in-video advertising to customers around the world. The company uses machine learning techniques to naturally blend brand advertising into popular entertainment content. Mirriad's technology supports in-video advertising, allowing brands to be placed in premium video content across TV, online, and mobile channels. The company's Chief Technology Officer, Tim Harris, explains that they achieve this by employing machine learning techniques that focus on annotating video metadata so they can pick the right piece of content for the right brand.
The Solution
Mirriad transitioned to Amazon Web Services (AWS), a process that took around a year. This transition instantly solved their capacity challenges and enabled them to set up customers 98% faster. The company uses AWS Auto Scaling across Amazon Elastic Compute Cloud (Amazon EC2) G2 and G3 instances for all of its video processing. This allows them to use between 20 and 40 GPU nodes for a typical production workload, which lasts around 15 to 20 minutes. The company also uses AWS Trusted Advisor to maintain and prove compliance, ensuring that their systems stand up to vigorous security audits.
Operational Impact
Quantitative Benefit
Related Case Studies.
Case Study
Integral Plant Maintenance
Mercedes-Benz and his partner GAZ chose Siemens to be its maintenance partner at a new engine plant in Yaroslavl, Russia. The new plant offers a capacity to manufacture diesel engines for the Russian market, for locally produced Sprinter Classic. In addition to engines for the local market, the Yaroslavl plant will also produce spare parts. Mercedes-Benz Russia and his partner needed a service partner in order to ensure the operation of these lines in a maintenance partnership arrangement. The challenges included coordinating the entire maintenance management operation, in particular inspections, corrective and predictive maintenance activities, and the optimizing spare parts management. Siemens developed a customized maintenance solution that includes all electronic and mechanical maintenance activities (Integral Plant Maintenance).
Case Study
Monitoring of Pressure Pumps in Automotive Industry
A large German/American producer of auto parts uses high-pressure pumps to deburr machined parts as a part of its production and quality check process. They decided to monitor these pumps to make sure they work properly and that they can see any indications leading to a potential failure before it affects their process.
