Machine Learning (ML) is a branch of artificial intelligence that deals with the development of algorithms and models that allow computers to learn from data and improve their decisions independently without explicit programming. Machine Learning is widely used for prediction, classification, pattern recognition, natural language processing, and other tasks.
To effectively train modern models, powerful GPU servers and specialized clusters are used to provide the necessary computational performance and scalability. This is a key technology for the development of artificial intelligence in many industries.
Key aspects of machine learning:
- Types of learning:
- Supervised learning - the model is trained on labeled data (inputs with known outputs).
- Unsupervised learning - the model is trained on unsupervised data, searching for hidden structures and patterns.
- Reinforcement learning - an agent learns to make decisions by receiving rewards for correct actions.
- Infrastructure for learning:
Machine learning requires significant computational resources, especially for training large models, such as large language models (LLMs). For this purpose, servers with powerful GPUs, specialized clusters, and supercomputers are used:- GPU clusters combine hundreds or thousands of gas pedals (e.g., NVIDIA DGX H100, AMD Instinct MI300X, Huawei models), providing high performance (of the order of PFLOPS or higher).
- There are various options for obtaining clusters: buying an off-the-shelf solution (e.g., NVIDIA SuperPOD), building it in-house, deploying it on cloud providers (AWS, Microsoft Azure, Meta RSC), building your own gas pedals (Google TPU, Tesla Dojo).
- Software platforms: TensorFlow, PyTorch and other frameworks are popular, well integrated with modern hardware to ensure speed and scalability.
- Applications: machine learning is used in a wide range of applications, from content recommendation and data analysis to automation, medicine, and autonomous systems.
- Features of modern clusters:
They provide fault tolerance, scalability, load balancing and fast communication between GPUs (via NVLink and NVSwitch), which greatly accelerates the training of complex models. - Trends: Large companies and cloud providers are investing in their own scalable systems and gas pedals to optimally address AI and machine learning challenges and reduce costs.
