Inference
Inference is the process of using a trained model to make predictions on new, unseen data. Optimizing inference speed and cost is critical for deploying AI in production applications.
Understanding Inference
Inference is the phase where a trained machine learning model processes new, unseen data to generate predictions, classifications, or other outputs. While training can take days or weeks on powerful GPU clusters, inference must often happen in milliseconds to support real-time applications like chatbots, recommendation systems, and autonomous vehicles. Optimizing inference speed and cost is a major engineering challenge, addressed through techniques like model quantization, knowledge distillation, pruning, and hardware acceleration with specialized chips from NVIDIA, Google (TPUs), and others. Batch inference processes many inputs together for throughput, while online inference handles individual requests with low latency. The economics of inference are a key consideration for deploying large language models at scale, as serving millions of users requires significant computational resources.
Category
Machine Learning
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Accuracy
Accuracy is a metric that measures the proportion of correct predictions out of total predictions made by a model. While intuitive, accuracy can be misleading on imbalanced datasets where one class dominates.
Active Learning
Active learning is a machine learning approach where the model selectively queries an oracle (often a human) for labels on the most informative data points. This reduces the total amount of labeled data needed to train an accurate model.
Anomaly Detection
Anomaly detection is the identification of data points, events, or patterns that deviate significantly from expected behavior. AI-based anomaly detection is used in fraud prevention, cybersecurity, and industrial monitoring.
AutoML
Automated Machine Learning (AutoML) is the process of automating the end-to-end pipeline of applying machine learning, including feature engineering, model selection, and hyperparameter tuning. AutoML democratizes AI by reducing the expertise required.
Bagging
Bagging (Bootstrap Aggregating) is an ensemble technique that trains multiple models on random subsets of training data and combines their predictions. Random Forest is the most well-known bagging-based algorithm.
Bayesian Network
A Bayesian network is a probabilistic graphical model that represents variables and their conditional dependencies using a directed acyclic graph. It enables reasoning under uncertainty and causal inference.
Bias-Variance Tradeoff
The bias-variance tradeoff is the fundamental tension in machine learning between model simplicity (high bias) and model flexibility (high variance). Optimal models balance underfitting and overfitting to generalize well to new data.
Binary Classification
Binary classification is a supervised learning task where the model assigns inputs to one of exactly two categories. Spam detection (spam vs. not spam) and medical diagnosis (positive vs. negative) are common examples.