Scott Data, a premier operator of traditional and high-performance Tier III Uptime Institute-certified data centers, is expanding its offerings with dedicated HPC solutions tailored for AI research and development, powered by Apolo AI platform.
Low-cost, low carbon bare metal provider for retail and wholesale customers in all the right places. Cato Digital is a member of the iMasons Climate Accord, reducing carbon in materials, products, and power, and it commits to tackling Scope-3 emissions.
A leading payment processing company, Centrobill provides secure and efficient online payment solutions for businesses worldwide. Centrobill ensures seamless transactions and compliance with global financial regulations.
A San Francisco-based AI infrastructure company needed robust MLOps on AWS to unblock scaling of their synthetic data platform.
PowerSetter is the leading digital platform for energy comparison, educating consumers about their available energy choices, allowing them to compare multiple energy suppliers, switch to the best one, and enroll in community solar programs.
Apply today, and enjoy up to $50,000 in value of combined benefits.
Scott Data, a premier operator of traditional and high-performance Tier III Uptime Institute-certified data centers, is expanding its offerings with dedicated HPC solutions tailored for AI research and development, powered by Apolo AI platform.
"By integrating Apolo's AI Platform, Scott Data is positioned at the forefront of HPC for AI, offering our customers cutting-edge performance with seamless operational management."
President & CEO, Scott Data
The AI revolution demands a new class of computing infrastructure capable of handling the immense processing requirements of cutting-edge ML models. The partnership between Scott Data and Apolo addresses this need by delivering HPC solutions with an operational backbone that significantly enhances the speed and efficiency of AI deployments.
Scott Data's commitment to high-performance computing is exemplified by deploying NVIDIA's DGX H100 machines, designed to meet the most demanding AI and ML workloads. Scott Data ensures that Apolo's AI platform is deeply integrated with these clusters, providing clients with a robust and intuitive system to effectively manage their AI development lifecycle.
The rapid advancement of AI technologies requires a specialized ecosystem that not only provides the necessary computational resources but also the infrastructure to manage these resources effectively. Traditional data centers must evolve to accommodate the unique demands of AI workloads without compromising on efficiency or sustainability.
FScott Data responds to this industry evolution by integrating Apolo's AI platform into its HPC offerings for AI. This integration represents a quantum leap in operational capability, allowing researchers and developers to easily access top-tier AI compute resources, manage workflows, and maintain data governance
The platform's core features provide a competitive edge by offering:
Additional features:
Together, Apolo and Scott Data are setting a new industry standard for HPC in the AI realm, fostering a synergistic environment where cutting-edge hardware meets sophisticated software, propelling the capabilities of AI forward.
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Low-cost, low carbon bare metal provider for retail and wholesale customers in all the right places. Cato Digital is a member of the iMasons Climate Accord, reducing carbon in materials, products, and power, and it commits to tackling Scope-3 emissions.
"Apolo’s approach to ML/AI models development, training, and inference perfectly aligns with our view of how sustainability should look in the data processing industry”
Cato Digital CEO
AI is a significant and growing driver of increased cloud usage in the data center industry, but teams require orchestration and integration support at each development and deployment stage. And all of these should come with a seamless solution to track the associated carbon footprint of training and a tool to reduce it.There is a growing demand for cloud-based AI infrastructure to support the AI associated workloads. Cloud providers invest heavily in building their AI capabilities to meet this demand, offering machine learning, natural language processing, and computer vision services.
AI progress, often driven by larger models, such as GPT-4, or more extensive data sets, comes at a real cost to the environment. Organizations of all sizes are increasingly conscious of the impact of their operations on the climate. The focus in designing and operating AI systems should be on energy efficiency, which can be achieved by using algorithms that demand minimal computational resources and removing unnecessary energy consumption.
AI’s portion of electricity consumption is growing much faster than other technologies. Deep Learning models and the data sets they train upon are increasing at a truly extraordinary rate – in the near times, the leading language model will have increased in size by over 100,000x.
The rapid advancement of AI technologies requires a specialized ecosystem that not only provides the necessary computational resources but also the infrastructure to manage these resources effectively. Traditional data centers must evolve to accommodate the unique demands of AI workloads without compromising on efficiency or sustainability.
Cato Digital is fully dedicated to constructing the world’s most sustainable bare metal platform. It is achieved using second-life hardware, stranded data center power capacity, and renewable energy. In alignment with the iMasons Climate Accord, Cato addresses scope-3 emissions as its contribution.
To facilitate AI workload growth and satisfy its current and future AI needs, Apolo installed its orchestration and interoperability MLOps solution to reside natively on Cato Digital data centers.
Moreover, the platform integrates a wide selection of best-in-breed AI/ML toolsets that cover the entire ML lifecycle. In an accelerated timeline, Apolo successfully installed, tested, and launched turnkey AI/ML services on Cato, enabling the company to leverage 100% green data center infrastructure.
Additionally, the platform’s functionality allows for multi-cloud and hybrid cloud architectures, and users can access pre-integrated AI/ML products, apps, and APIs – encompassing open-source and proprietary options. This provides users with several benefits, including cost, time, difficulty, and risk reduction for their AI development projects.
Additional features:
PowerSetter is the leading digital platform for energy comparison, educating consumers about their available energy choices, allowing them to compare multiple energy suppliers, switch to the best one, and enroll in community solar programs.
“PowerSetter is saving an average of $6,300 per month on high-performance computing costs, which has been a substantial benefit for our growth and operations. We're now able to provide faster and more personalized energy solutions to our users."
Co-founder and CEO
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As the largest digital energy comparison platform, PowerSetter recognized the growing need to enhance its services and provide consumers with more efficient and personalized energy solutions. With the energy market evolving rapidly and consumers seeking greater control over their energy choices, PowerSetter saw an opportunity to leverage AI and other advanced technologies to revolutionize the energy comparison experience.
PowerSetter faced several challenges in achieving its vision of delivering a cutting-edge energy comparison platform. These challenges included the need to process vast amounts of data quickly and accurately, provide real-time insights to consumers, and ensure scalability to accommodate a growing user base. Additionally, the platform required sophisticated machine learning and AI capabilities to analyze consumer preferences and offer personalized energy recommendations.
To address these challenges, PowerSetter partnered with Apolo and implemented the Apolo GPU Hub & AI-Centric Ecosystem. By leveraging Apolo's comprehensive suite of tools and resources, PowerSetter was able to transform its platform into an even more dynamic and intelligent energy comparison solution. Apolo's HPC resources enabled PowerSetter to process large datasets with unparalleled speed and efficiency, while the integrated AI Platform and ML development toolkit empowered PowerSetter to deliver personalized energy recommendations to consumers in real-time.
Furthermore, Apolo's flexible deployment options allowed PowerSetter to deploy the solution in a distributed architecture, ensuring optimal performance and scalability. Whether as a dedicated enterprise cluster or a multi-tenant white-label solution, Apolo provided PowerSetter with the flexibility and reliability needed to support its growing user base and evolving business needs.
By partnering with Apolo, PowerSetter successfully transformed its digital energy comparison platform into a market-leading solution that empowers consumers to make informed energy choices. With Apolo's advanced technologies, services, and scalable infrastructure, PowerSetter can continue to innovate and deliver unparalleled value to its users, driving greater energy efficiency and sustainability.
This case study highlights how Apolo's GPU Hub & AI-Centric Ecosystem revolutionized PowerSetter's energy comparison platform, demonstrating the transformative impact of advanced technologies in the energy industry.
A leading payment processing company, Centrobill provides secure and efficient online payment solutions for businesses worldwide. Centrobill ensures seamless transactions and compliance with global financial regulations.
"Partnering with Apolo has significantly improved our fraud detection capabilities, allowing us to provide a safer and more reliable payment processing service to our clients. The advanced AI and machine learning models have been a game-changer for us."
Founder of Centro bill
Centrobill, a leading worldwide payment processing company, recognized the increasing need to bolster its fraud detection mechanisms. With the rise in digital transactions and the sophistication of fraudulent activities, Centrobill saw an opportunity to leverage advanced technologies to enhance its fraud detection capabilities. By improving its ability to detect and prevent fraud, Centrobill aimed to provide a safer and more reliable payment processing service for its clients.
Centrobill faced several significant challenges in its quest to enhance fraud detection. The company needed to process vast amounts of transaction data quickly and accurately to identify potential fraud patterns. Real-time analysis was crucial to prevent fraudulent activities before they could impact clients. Additionally, the solution needed to be scalable to handle the growing number of transactions and evolving fraud tactics. Advanced analytics, including sophisticated AI and machine learning capabilities, were necessary to develop more accurate and efficient fraud detection models.
To tackle these challenges, Centrobill partnered with Apolo to implement a scalable, high-performance computing (HPC) AI-driven solution. Apolo provided a comprehensive suite of tools and resources that transformed Centrobill's fraud detection capabilities. Apolo's GPU Cloud allowed Centrobill to process large volumes of transaction data with exceptional speed and efficiency. The scalable HPC cloud infrastructure enabled the rapid analysis of complex data sets, essential for identifying subtle fraud patterns. Apolo's AI platform and machine learning development toolkit empowered Centrobill to create advanced fraud detection models. These models could analyze transaction data in real-time, identifying and flagging suspicious activities with high accuracy. Apolo's flexible deployment options ensured that Centrobill could scale its fraud detection solutions to meet increasing transaction volumes. Apolo provided the reliability and performance necessary to support Centrobill's growing needs.
By partnering with Apolo, Centrobill significantly enhanced its fraud detection capabilities. The advanced AI and machine learning models provided more accurate detection of fraudulent activities, reducing false positives and negatives. The ability to analyze transactions in real-time allowed Centrobill to prevent fraud before it could affect clients. Apolo's scalable infrastructure ensured that Centrobill could handle the growing volume of transactions and evolving fraud tactics. By providing a safer and more reliable payment processing service, Centrobill strengthened trust and satisfaction among its clients.
A San Francisco-based AI infrastructure company needed robust MLOps on AWS to unblock scaling of their synthetic data platform.
“Within a month of our migration from Kubeflow to Apolo on AWS, we tripled the number of ML experiments we could run.”
CEO Synthesis AI
According to market research firm Omdia¹, the AI computer vision market is expected to reach $33.5 billion by 2025.
To date, computer vision driven by deep learning has been expensive and hard to scale as it relies heavily on supervised learning that requires human-in-the-loop labeling of key image attributes. Besides the time and cost required for manual labeling, there are also significant ethical and privacy issues connected to the use of real-world data.
All of these issues are effectively solved by Synthesis AI’s synthetic data technology. By combining CGI technologies with novel generative AI models, their simple API enables the programmatic generation of millions of images with pixel-perfect labels. Further, Synthesis AI’s synthetic data can often provide an even higher quality result than real images.
With individual client demands exceeding hundreds of millions of synthetic data images per month, Synthesis AI needed to build a robust and scalable infrastructure from day one.
Like many AI-focused companies, Synthesis AI initially assigned their internal ML engineering team the task of building and maintaining their MLOps infrastructure (including coordination and management of on-prem and cloud compute resources, data, models, pipelines and workflows). For this purpose, the team chose Kubeflow, a popular open-source ML development platform, running on AWS as the foundation upon which they would build their ML development lifecycle.
After 6 months of building and re-building on Kubeflow, the team realized that they were spending as much time on MLOps as they were on ML. Their synthetic data and AI pipelines required constant maintenance, they needed to manually integrate and update every tool they sought to use, and managing their computation resources on AWS and on-prem required constant attention and maintenance. They realized that Kubeflow itself is not a scalable MLOps solution.
Furthermore, Synthesis AI was limited by their cloud provider, facing allocation and infrastructure maintenance issues that severely complicated their model training process.
Synthesis AI needed solutions for both MLOps and cloud computing that allowed their ML Engineers to focus on the models.
First, Apolo migrated the company’s entire ML workflow from Kubeflow to Apolo Platform, all completely within their secure AWS environment. Second, Apolo engaged AWS to provide a long-term solution to Synthesis AI’s computational resource requirements, ensuring both scalability and availability. These migrations included:
As a result, Apolo unblocked Synthesis AI to scale its synthetic data platform. Synthesis AI’s ML productivity tripled in the first month alone, increasing the number of training jobs by 10x, while saving over $100,000 in computing costs.
Apolo turns your Data Center into an AI Powerhouse.
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