Blog Detail

The advancements in AI and ML can come at the expense of your infrastructure. Training massive machine learning models, managing petabytes of information, and running complex algorithms all require high-performance cloud-based environments with unlimited scalability. Time to build high-performance cloud platforms that bridge the gap between existing infrastructure and the fast-paced innovation expected from enterprise teams and technology-driven organisations.

Nothing about the cloud should limit what you create. Your innovations should not be constrained by slow computing, long provisioning cycles, or fragmented data pipelines. Organisations must evolve toward cloud infrastructure designed to support modern AI and ML workloads, enabling scalability, reliability, and security aligned with enterprise requirements.

Cloud Computing Evolution

While cloud infrastructure was originally viewed as nothing more than a place to host files and deliver simple virtual computers, it has become an important tool that serves as a powerful foundation for many different types of computing activity, including, artificial intelligence (AI) and machine learning (ML), to scale the amount of computational power and thus, allow for the development of more complex AI Models.

Building complex AI models requires a tremendous amount of processing power, as well as scalable architectures. When combined with GPU enabled equipment, cloud platforms provide the means for organisations to move quicker through their development cycle by streamlining their workflow for both their data intake and data processing; this allows teams to create AI models much faster and in a more cost-effective way than in their own data centre, thus giving organisations more flexibility to adjust their strategies to rapidly changing market conditions and to leverage the ever-growing speed of technology advancements.

GPU-Powered Performance with NVIDIA

The development of parallel processing capabilities, which are essential for machine learning and deep learning algorithms, has made GPU computing the backbone of modern AI applications. Cloud service providers (CSPs) that offer infrastructure built on NVIDIA GPUs enable their customers to build scalable and efficient workloads to run AI applications.

Organisations that use a CSP's GPU-enabled cloud infrastructure can develop training models more quickly with consistent performance on all workloads and can efficiently perform complex AI tasks and process vast amounts of data. This method allows organisations to avoid the costs associated with the heavy upfront investment to establish on-premise GPU clusters while allowing for the use of a cloud service provider that provides a flexible way to scale GPU resources.

Pre-Installed AI/ML Frameworks

To successfully deploy AI-driven software, the time between generating an idea and releasing the product is extremely important. Teams developing AI-based products should spend time on building models and assembling the appropriate infrastructure components rather than configuring those components. The cloud environment is already set up so that the team can start building products right away using the standard AI/ML frameworks that have been widely adopted in the industry.

 The cloud-based platform offers the ability to integrate with commonly used artificial intelligence (AI) software applications and machine learning (ML) software applications, including TensorFlow, PyTorch, Keras, and Scikit-learn. This integration allows the team to make efficient use of GPUs for developing and testing their models. Reduction of setup overhead and increase in productivity allow for more efficient and effective use of the AI initiatives that are deployed.

Scalable Storage Solutions

The basis of AI and ML workloads is data. For the computing resources associated with a high-performance computing environment to function without being impeded by bottlenecks, the storage architectures within this environment must also be at least as effective as the computing resources. A proper AI Cloud platform will provide storage options that scale with performance and capacity requirements.

 High-speed block storage solution (for example, SSD or NVME) works well for workloads that need to minimise latency and maintain consistent performance. On the other hand, S3 Object storage (compatible) allows businesses to store large amounts of unstructured data (datasets, model artefacts, and media files) to be used in both Training workflows and Deployment workflows.

 A properly designed cloud infrastructure must integrate compute and storage layers so that both layers operate together as one system, for maximum performance and reliability, in addition to allowing for resilient and efficient access to data at scale.

Secure Cloud Databases

For enterprise Artificial Intelligence (AI) applications, the availability and integrity of data are vital for the application to fulfil its purpose. Our platform offers managed cloud database services to accommodate the operational and analytical workloads created by Artificial Intelligence (AI) driven systems. These database services are designed according to the best practices of the enterprise for protecting data, controlling access to data, and ensuring the availability of data for both structured and unstructured data types. In addition, by utilising the best practices for protecting data through security throughout the storage, compute, and network layers, organisations can maintain a high level of trust and reliability, while also having the capability to grow Artificial Intelligence (AI) applications.

AI Lab as a Service

In order to transition Artificial Intelligence (AI) initiatives to production it has been necessary to develop an organised and coordinated environment to support collaboration amongst teams. AILaaS (AI Lab as a Service) is a cloud-based platform that provides organisations with a common infrastructure where teams can easily create, develop, and assess AI models without the need for additional hardware.

An AILaaS solution provides an organisational structure to facilitate collaboration by providing a centralised location for organisations to collaborate on their AI labs and utilising managed compute resources, shared development environments, and tools that support AI project workflows, thereby simplifying AI implementation. With AILaaS, organisations can eliminate unnecessary complexity related to their infrastructure, allowing them to concentrate on generating innovative ideas and creating value through the implementation of artificial intelligence.

Global CDN and Performance Optimisation

AI workloads include more than just the training and development of AI Models and Applications. The Cloud Infrastructure is also made to support the Delivery of AI Models and Applications reliably and efficiently, and a Cloud Infrastructure needs both the Networking Capabilities and optimisation capabilities that are integrated into both of those features that are necessary to support the Delivery of AI Solutions.

The use of Managed Load Balancing helps to distribute traffic to the Compute Resources that are utilised for the Delivery of AI Solutions to maintain the Availability of AI Solutions and accommodate changing demands. Global Content Delivery Networks (CDN) also assist with reducing Latency by distributing content through many different geographic locations. This enables a Better User Experience and Improved Application Performance on a large scale.

The Solution: VTL’s (Vensysco Technologies Limited) High-Performance Cloud Platform

With Vensysco Technologies Limited's cloud solutions, organisations can leverage modern AI-ready infrastructure to reduce provisioning time and improve overall innovation velocity. Our Enterprise Cloud Solutions are designed for AI and Machine Learning workloads, built on high-performance, GPU-enabled cloud infrastructure aligned with industry-standard cloud architectures. With our solutions, you can take advantage of compute power delivered through enterprise-grade, NVIDIA GPU-accelerated infrastructure, AI Frameworks built in Ready to Go, Enterprise Storage that grows as your company grows, Database Hosting and Security (Database-as-a-Service), and AI Lab as a Service (AILaaS).

VTL (Vensysco Technologies Limited) Cloud Solutions are architected using industry-standard engineered Reliability/Performance/Security with Managed Load Balancing, Global Content Delivery Network (CDN), and Enterprise Security Control Systems for your protection. With Vensysco Cloud Solutions, you can develop, deploy, scale, and operate AI applications while maintaining control, resiliency, and compliance through enterprise-grade security, governance, and access control practices.