Artificial intelligence has been actively developing in recent years. It is a labor-intensive technology that requires a large amount of resources, including GPU resources. At the same time, there are currently problems in the European market with the supply of the necessary components for AI clusters.
It is very difficult to purchase the necessary configuration of video cards for machine learning in the required quantities, and their cost is very high, which prevents businesses from forming their own clusters for ML and limits their further scaling.
Cloud providers are coming to the rescue of businesses, offering the opportunity to do this with a smaller budget and the ability to scale up in a timely manner.
Cloud technologies are the very environment that helps artificial intelligence develop and learn by leaps and bounds.
At the same time, thanks to AI, the cloud solutions market itself is also developing. For example, in a review by Fortune Business Insight, AI technologies for processing, analyzing, and displaying video content are named as one of the key reasons for the growth of the global market for cloud services for video to $10.28 billion in 2024.
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Advantages of cloud platforms for AI
Cloud technologies in machine learning open up broad horizons for the development, testing, training, and implementation of artificial intelligence models.
They provide the very foundation that allows for the full-scale deployment of the computing capabilities necessary for full-fledged machine learning and AI development.
Why do cloud computing and artificial intelligence have such a strong and mutual influence? What advantages can cloud technologies offer?
Versatility and scalability. Cloud solutions allow you to dynamically scale computing power according to your needs. This is especially important for complex models that require extensive data and significant computing resources.
Access to powerful tools and libraries. Cloud providers offer ready-made frameworks for data processing and model deployment, such as TensorFlow, PyTorch, Scikit-learn, and others.
Integration with other systems: the ability to connect to databases, IoT devices, and other cloud services.
Resource optimization and cost management. AI in the cloud promotes efficient resource allocation and optimization of computing power usage, which reduces equipment and infrastructure maintenance costs.
Availability. No major investments are required to implement machine learning-based projects — the provider supplies computing resources, including high-performance GPUs, on demand as part of a flexible pricing model.
Speed of launch. Unlike setting up your own on-premises AI cluster, launching an AI cluster in the cloud takes just a couple of clicks.
There is no need to think about cluster maintenance and availability, which saves not only nerves but also money on highly qualified specialists.
Conclusion
The use of artificial intelligence can be a serious driver of business development if its implementation is properly organized and the necessary resources for its operation are provided. Cloud technologies provide both an excellent field for deploying the necessary artificial intelligence and machine learning systems, and the opportunity to do so without additional costs for equipment, software, and specialists.