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Artificial intelligence (AI) has revolutionized the way we approach problem-solving, from automating routine tasks to making complex decisions. As businesses and industries continue to adopt AI, understanding the lifecycle of AI is crucial for effectively implementing and scaling these technologies. AI inference and training, both involve a series of steps that take a model from conception through to deployment and continuous learning. In this blog, we will discuss some of the fundamental steps in the AI lifecycle—one that is, by design, artificially intelligent.
In this blog, we will discuss some of the fundamental differences between AI inference vs. training—one that is, by design, artificially intelligent.
Although the term training is self-explanatory, let's dive a little deeper into its definition.
AI training is the process that follows after choosing an algorithm based on the problem at hand. During training, the model learns patterns from the data it is fed. This step is essential for making the model robust and capable of making accurate predictions later on. Essentially, training involves exposing the model to large amounts of labeled data, including inputs and corresponding correct outputs. The model adjusts its internal parameters (called weights) to minimum.
Let’s illustrate this with an example: Suppose you recently joined a new organization as a Project Manager, but you are a fresher. There was no formal training program, so you are thrown into the work and expected to figure things out—handling Jira boards, understanding Azure or GitBoards, and managing sprints. Initially, you would find this overwhelming, and it would take a while to get up to speed. Eventually, though, you'd learn how things work.
Similarly, without going through AI training, you can’t jump straight into AI inference. The training phase is crucial for preparing the model to make sense of the data and deliver useful predictions.
Training an AI model requires significant computational resources. If the data is large, GPUs are often required to speed up the training process. CPUs can work for smaller datasets, but GPUs offer a considerable performance boost—though they come at a cost.
Data Collection: Gather large datasets relevant to the problem the AI model is trying to solve.
Model Selection: Choose the AI model that best fits the problem (e.g., neural networks, decision trees, etc.).
Training Process: The model processes the data and adjusts its internal parameters based on feedback (using algorithms like backpropagation in deep learning).
Optimization: Through repeated iterations, the model fine-tunes its parameters to reduce errors and improve accuracy.
Contradictory to AI training, inference is about the training process to get complete (think of the example of the trained Project Manager),. The model has learned from the data, and now it needs to demonstrate what it has learned, but not on the same data it was trained on. Instead, it will be tested on new, unseen data.
So, in AI inference, the trained model is given new data and asked to make predictions based on what it has learned during training. The model doesn't adjust its parameters at this stage—this is simply a prediction phase, applying the model's knowledge to real-world situations.
The primary goal of building an AI application is to make predictions. After training the model, inference allows us to assess whether the model has effectively learned the patterns and can make accurate predictions on new data.
AI inference requires the saved model, which it uses to make predictions. Unlike training, which often demands powerful computational resources, inference does not need high-end hardware. It’s more about applying the knowledge gained during training to make predictions.
Deployment: The trained model is deployed in production environments, where it can process new data in real-time or through batch processing.
Prediction: The model uses its learned knowledge to analyze new inputs and make predictions.
Speed: Inference is designed to be much faster than training, allowing for real-time decision-making.
Low Resource Requirements: Since the model is not adjusting its parameters, inference typically requires fewer computational resources than training.
Deciding when to focus on AI training versus inference is simple once you understand their roles especially in AI inference and training. When you have data to help your model learn patterns, you begin with training. When you're ready to make predictions on new, unseen data, it’s time for inference.
At Enqurious, we guide professionals through both stages, offering hands-on learning and real-world use cases. Our platform ensures that learners are equipped to move from model creation to deployment, building impactful AI solutions that drive business success. Talk to us today to learn more!
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