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AI Model Training and Development
There are multiple stages in developing and deploying machine learning models, including training and inferencing. AI training and inferencing refers to the process of experimenting with machine learning models to solve a problem.
For example, a machine learning engineer may experiment with different candidate models for a computer vision problem, such as detecting bone fractures on X-ray images.
To improve the accuracy of these models, the engineer would feed data to the models and tune the parameters until they meet a predefined threshold. These training needs, measured by model complexity, are growing exponentially every year.
Infrastructure technologies key to AI training at scale include cluster networking, such as RDMA and InfiniBand, bare metal GPU compute, and high performance storage.
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