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AI: In the Pipeline

18 December 2023

ai pipeline

Ever wondered how AI is made? In this article, let’s delve into the pipeline of creating AI.

 

The AI pipeline comprises three main phases: Data Management, Model Training, and Inference. Each phase comprises two primary steps in AI development. Data management and model training constitute the initial step, which is AI creation. Inference forms the subsequent step, marking AI deployment. In the Data Management phase, data scientists handle massive data volumes, uncovering patterns and insights crucial for preparing training datasets. This step is pivotal in readying the training data set used to train a model for specific tasks. Once training concludes, the AI is created, and the trained model is deployed to perform its tasks—a process known as Inference.

ai pipeline

Source: Nvidia

Model Training involves training the AI for specific tasks. For instance, if you desire an AI to identify animal types based on pictures, start by exposing it to numerous photos of the specific animal to enable generalization. Richer data enhances the accuracy of the AI model. The subsequent refinement involves distinguishing the given animal from others, exposing the model to various animals for learning. Repeated validation and testing hone the model’s abilities, ensuring higher predictive accuracy upon deployment. However, this phase is time-consuming due to its iterative nature.

 

Finally, the trained model is deployed to tackle real-world scenarios with unseen data, known as inference.

 

Each of the two primary steps requires different types of computing power. In AI creation (model training), the most computationally intense phase, traditionally CPUs are used to train AI models. However, CPUs are ill-suited for big data computing, creating bottlenecks during model training. Here, GPUs step in to eliminate these bottlenecks.

 

In Inference, though less computationally intensive compared to model training, demands rapid real-time performance at scale. For instance, waiting minutes for a navigation app to inform about a wrong turn is undesirable. Hence, GPUs are essential in the inference phase for efficient performance. At Aperia Cloud Services, we understand the challenges in AI utilization. Whether enterprises are initiating their AI journey or scaling up, we provide the computational power and scalability necessary. This enables our clients to focus on analytics, solution-building, and delivering substantial business value.

Source: ACS

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