OVHcloud dedicated servers for AI and machine learning
8 cores / 16 threads - 2.1 GHz
14 cores / 28 threads - 2.6 GHz
With the rise of big data, AI and machine learning methods have rapidly moved from purely conceptual to powerful business tools, with the potential to deliver invaluable insights and sustainable growth. With companies producing and more data than ever before – all of which will require processing, classification and analysis – organisations ranging from startups to global leaders are exploring how artificial intelligence, and an increasingly sophisticated range of machine learning algorithms, can be utilised for a range of applications, including:
- Business intelligence
- Predictive analytics
- Artificial neural network
- Image recognition
- Structural analysis
- Speech recognition
- Fraud detection
- Face recognition
- Anomaly detection
- Pattern recognition
- Deep learning frameworks and libraries
- Datacentre automation and maintenance
- Graphical models
- Statistical modelling
- Natural language processing
- Specialist financial services, such as algorithmic trading, market analysis and portfolio management
- Scientific research, including genomics, computational chemistry and modelling/simulation projects
Bare metal or cloud solutions (or both!) for AI and machine learning?
AI and machine learning projects are inherently resource-intensive, on account of the enormous data sets utilised, and the need to process high volumes of unstructured data with sophisticated mathematical models. This means that data scientists demand the highest level of computing power and performance is required. Bare metal’s dedicated resources are therefore ideal for AI, but there are also several cloud solutions – including container-based ones and clustering algorithms – that can serve as the launch-pad for your projects, and help transform large quantities of unstructured data into usable, structured data.
If you do wish to start your AI projects in the cloud – with a view towards moving towards dedicated servers, or a tailored hybrid infrastructure – OVHcloud’s cloud infrastructure offers the option of deploying multiple AI software solutions, optimised for NVIDIA GPUs (graphical processing units), including the V100 Tensor Core.
Tip 1. Would you be better served by an open-source solution or commercially available data science software when developing your own machine intelligence platform in-house?
There are currently numerous open-source and commercially-available AI/machine learning software solutions, which you are free to deploy on any OVHcloud HG servers, along with your own choice of operating system, via root access. Working with open-source solutions (such as Kaggle, Hadoop clustering and associated tools) provides tangible cost savings compared to commercial software, particularly in projects’ nascent stages. This can prove advantageous during the proof-of-concept, for example, and help you upscale smoothly and intelligently, with plenty of scope for customisation as you do so (including your data security measures). On the other hand, cloud-based SaaS solutions, such as OVHcloud’s Analytics Data Platform, allow you to access a tried-and-tested solution quickly, whenever and wherever you need it.
Tip 2. Design your solution with full redundancy in mind
As with any high-powered IT project, designing your infrastructure with redundancy in mind is a key part of achieving consistently high performance with your AI projects. By deploying your AI/machine learning system on OVHcloud’s global infrastructure, you will enjoy access to our full network of datacentres, with the ability to deploy new servers and cloud instances, and create secure, private networks between your solutions using the OVHcloud vRack, with full redundancy and robust disaster recovery plans. This will ensure your servers will always be able to deliver the optimal performance demanded by artificial intelligence, with zero compromise in terms of security, and full compliance with all applicable data protection regulations.
Tip 3. Consider how you plan to upscale your AI/machine learning projects in the future
When you’re exploring AI and machine learning systems, it’s important that your chosen solutions will be able to evolve as your business analytics requirements do. If you plan on scaling with cloud and bare metal, you need to be able securely transfer your data, and interconnect physical and virtual solutions. Your cloud provider should provide you with complete freedom to migrate your data in the way that’s right for you, with tools and solutions for smooth, secure migrations and interconnections readily available.