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Apolo > Case Studies > Zero-Emissions AI Infrastructure: A Case Study of atNorth and Neu.ro
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Zero-Emissions AI Infrastructure: A Case Study of atNorth and Neu.ro

Technology Category
  • Analytics & Modeling - Machine Learning
  • Infrastructure as a Service (IaaS) - Hybrid Cloud
Applicable Industries
  • Cement
  • Construction & Infrastructure
Applicable Functions
  • Facility Management
  • Product Research & Development
Use Cases
  • Construction Management
  • Infrastructure Inspection
Services
  • Cloud Planning, Design & Implementation Services
  • Training
The Challenge

The cloud revolution is in full force, with AI being a major driver of new cloud adoption. IDC estimates that fast-growing AI workloads will account for up to 50% of the total cloud market by 2025. However, this progress comes at a significant cost to the environment. Information and Communications Technologies (ICT) already account for an estimated 9% of total global electricity use, a figure that could more than double by 2030. AI’s portion of electricity consumption is growing much higher than other technologies. Deep Learning models and the data sets they train upon are growing at an extraordinary rate. In less than 5 years, the leading language model will have increased in size by over 100,000x. The competitive challenge presented by hyperscale CSP AI development platforms (Azure Machine Learning, AWS Sagemaker, GCP AI) required an immediate response. Neu.ro provided a market proven platform with unique advantages.

About The Customer

The customer, atNorth, is a leading Nordic data center services company offering environmentally responsible, power-efficient and cost-optimized hosting facilities and high-performance computing services. They offer sustainable and extremely scalable cloud HPC resources, fully enabling customers to focus on their simulation applications and calculations without having to worry about the underlying HPC infrastructure. The company operates in the zero-emissions IT infrastructure and cloud services industry. They are committed to providing solutions that are not only efficient and effective but also environmentally friendly.

The Solution

To facilitate AI workload growth and satisfy atNorth’s current and future AI needs, Neu.ro proposed to install an oem cloud AI orchestration and interoperability solution to reside natively on atNorth’s zero emissions cloud service. The unique advantages of the platform are easy and fast access to the computing infrastructure from a CLI or menu system, access control and permissions for authorized team members, orchestration of both cloud and on-prem compute resources, automation of workflows and protection of AI assets and artifacts at all stages of the ML lifecycle. The platform also integrates a wide selection of best-in-breed AI/ML toolsets covering the entire ML life cycle. Following an accelerated timeline, Neu.ro successfully installed, tested and launched turnkey AI/ML services for atNorth, allowing them to leverage their existing corporate client base by offering a compelling alternative to AWS Sagemaker and offering ease and flexibility for creating, training, testing and deploying models.

Operational Impact

  • The solution provided by Neu.ro has allowed atNorth to offer a compelling alternative to other major AI development platforms such as AWS Sagemaker. This has enabled them to leverage their existing corporate client base, offering ease and flexibility for creating, training, testing and deploying models. The platform also provides additional functionality permitting multi-cloud and hybrid cloud architectures and a selection of ready and pre-integrated AI/ML products, apps and APIs for use. This provides users with advantages in terms of reduced cost, time, difficulty and risk for AI development projects. Furthermore, the platform includes an integrated toolset to manage the environmental impact of AI programs, allowing users to monitor the CO2 footprint of every job, model, project, team and optimize compute usage to balance performance and environmental impact.

Quantitative Benefit

  • 100% powered by geothermal & hydro generation

  • AI’s portion of electricity consumption is growing much higher than other technologies

  • In less than 5 years, the leading language model will have increased in size by over 100,000x

Related Case Studies.

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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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