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cnvrg.io > Case Studies > Achieving Massive Business Growth with Large-Scale Models in Production

Achieving Massive Business Growth with Large-Scale Models in Production

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Technology Category
  • Infrastructure as a Service (IaaS) - Hybrid Cloud
  • Platform as a Service (PaaS) - Application Development Platforms
Applicable Industries
  • Cement
  • Construction & Infrastructure
Use Cases
  • Construction Management
  • Infrastructure Inspection
Services
  • Cloud Planning, Design & Implementation Services
  • Data Science Services
About The Customer

Wargaming is an award-winning online game developer and publisher, and one of the leaders in the free-to-play MMO market. The company delivers authentic gaming experiences and services across PC, console, and mobile platforms. With over 110 million players worldwide, 15+ game titles, and almost 2PB of data, Wargaming has successfully scaled AI that has driven exponential growth over the last few years. Their data science and engineering teams have 1,500+ models running in production today that power dynamic in-game experiences, improve support efficiency, and deliver advanced reporting systems that drive critical business decisions.

The Challenge

Wargaming, an award-winning online game developer and publisher, faced a significant challenge in scaling their AI across business units. With over 110 million players worldwide, 15+ game titles, and almost 2PB of data, the company had 1,500+ models running in production on a single server solution. This severely limited infrastructure and constrained data scientists, as the overhead cost of adding servers was extremely high due to per core licensing. The existing platform also limited data scientists to a language and packages pre-approved by the platform. Wargaming needed a solution that could support large-scale models in production, scale their servers, minimize overhead costs, and provide flexibility for their data scientists.

The Solution

Wargaming adopted cnvrg.io, a modern MLOps automation platform, to support their massive amount of models in production. This platform provided fully automated pipelines across their product, support, and business reporting units. Compared to their legacy platform, cnvrg.io delivered a modern Kubernetes and cloud native based infrastructure that easily blended with existing IT and offered the ability to grow without limitations. Wargaming was able to utilize the powerful machines they already had, and easily add acceleration and powerful servers to achieve even better performance. The data scientists were able to quickly adapt to cnvrg.io with an intuitive UI that gave them the flexibility to use their preferred language, add customized packages and use other open source capabilities.

Operational Impact

  • By adopting cnvrg.io, Wargaming gained flexibility and scale that they never could with their legacy platform. The platform enabled Wargaming to fully automate 1500 models in production while accelerating continuous application delivery. They achieved results at the same performance in half the time and at half the cost of their legacy platform. cnvrg.io enabled them to seamlessly adopt new servers and accelerators to improve performance. They saved money and time by not having to purchase and install a new bare metal machine from their previous vendor. Now, they can scale seamlessly to a hybrid cloud environment, add accelerators and grow to maximize performance. They have accelerated their ML pipeline by more than 50% by eliminating delays from ML platform developers. Now, data scientists can run their entire ML workflow autonomously, without DevOps or platform developers. In addition, Wargaming has achieved a nearly impossible goal, approval from the data science team. For the first time, Wargaming’s critical team of data scientists are lining up to use cnvrg.io.

Quantitative Benefit

  • Reduced cost by more than 50% on servers and licensing

  • Accelerated research to production from 3 months to 2 months

  • Accelerated time to production by 30% by eliminating bottlenecks

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Case Study
IoT System for Tunnel Construction
The Zenitaka Corporation ('Zenitaka') has two major business areas: its architectural business focuses on structures such as government buildings, office buildings, and commercial facilities, while its civil engineering business is targeted at structures such as tunnels, bridges and dams. Within these areas, there presented two issues that have always persisted in regard to the construction of mountain tunnels. These issues are 'improving safety" and "reducing energy consumption". Mountain tunnels construction requires a massive amount of electricity. This is because there are many kinds of electrical equipment being used day and night, including construction machinery, construction lighting, and ventilating fan. Despite this, the amount of power consumption is generally not tightly managed. In many cases, the exact amount of power consumption is only ascertained when the bill from the power company becomes available. Sometimes, corporations install demand-monitoring equipment to help curb the maximum power demanded. However, even in these cases, the devices only allow the total volume of power consumption to be ascertained, or they may issue warnings to prevent the contracted volume of power from being exceeded. In order to tackle the issue of reducing power consumption, it was first necessary to obtain an accurate breakdown of how much power was being used in each particular area. In other words, we needed to be able to visualize the amount of power being consumed. Safety, was also not being managed very rigorously. Even now, tunnel construction sites often use a 'name label' system for managing entry into the work site. Specifically, red labels with white reverse sides that bear the workers' names on both sides are displayed at the tunnel work site entrance. The workers themselves then flip the name label to the appropriate side when entering or exiting from the work site to indicate whether or not they are working inside the tunnel at any given time. If a worker forgets to flip his or her name label when entering or exiting from the tunnel, management cannot be performed effectively. In order to tackle the challenges mentioned above, Zenitaka decided to build a system that could improve the safety of tunnel construction as well as reduce the amount of power consumed. In other words, this new system would facilitate a clear picture of which workers were working in each location at the mountain tunnel construction site, as well as which processes were being carried out at those respective locations at any given time. The system would maintain the safety of all workers while also carefully controlling the electrical equipment to reduce unnecessary power consumption. Having decided on the concept, our next concern was whether there existed any kind of robust hardware that would not break down at the construction work site, that could move freely in response to changes in the working environment, and that could accurately detect workers and vehicles using radio frequency identification (RFID). Given that this system would involve many components that were new to Zenitaka, we decided to enlist the cooperation of E.I.Sol Co., Ltd. ('E.I.Sol') as our joint development partner, as they had provided us with a highly practical proposal.
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