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Design Guide—Generative AI in the Enterprise - Inferencing
Introduction AI Model Inferencing
What is inferencing?
Inferencing use cases
Challenges with inferencing
Inferencing concepts
Solution Components Solution Architecture Validation Results Performance Characterization and Sizing Inferencing Operations Dell Professional Services for Generative AI Conclusion References

What is inferencing?

What is inferencing?

  • Inferencing in generative AI is the process of using a pretrained model to generate predictions, make decisions, or produce outputs based on specific input data and contexts. It applies the learned knowledge and patterns acquired during the model's training phase to respond with new and unique content. It represents a crucial step in leveraging the capabilities of generative models for a wide range of applications.

    At the core of generative AI is a model that has been trained on vast amounts of data to understand and generate human-like text, images, or other forms of content. During the training phase, the model learns to recognize patterns, relationships, and structures in the data. For instance, a language model like Generative Pre-trained Transformer (GPT) learns the statistical properties of language, enabling it to generate coherent and contextually relevant text.

    During inferencing, the trained model processes input data through its computational algorithms or neural network architecture to produce an output or prediction. The model applies its learned parameters, weights, or rules to transform the input data into meaningful information or actions.

    Inferencing is the culminating and operational stage in the life cycle of an AI system. After training a model on relevant data to learn patterns and correlations, inferencing allows the model to generalize its knowledge and make predictions or generate responses that are accurate and appropriate to the specific context of the business.

    For example, in a natural language processing task like sentiment analysis, the model is trained on a labeled dataset with text samples and corresponding sentiment labels (positive, negative, or neutral). During inferencing, the trained model takes new, unlabeled text data as input and predicts the sentiment associated with it.

    Inferencing can occur in various contexts and applications, such as image recognition, speech recognition, machine translation, recommendation systems, and chatbots. It enables AI systems to provide meaningful outputs, help with decision-making, automate processes, or interact with users based on the learned knowledge and patterns captured by the model during training. Generative AI inferencing is what allows AI systems to produce coherent and contextually relevant responses or content in real time.