‘Inference’ is the act or process of reaching a conclusion about something from known facts or evidence. ‘Prediction’ is a statement about what will or might happen in the future. ‘Summarizing’ is taking a lot of information and creating a condensed version that covers the main points.

A statement, which tells or estimates something that will occur in future is known as the prediction. The hypothesis is nothing but a tentative supposition which can be tested by scientific methods. Hypothesis always have an explanation or reason, whereas prediction does not have any explanation.

In general, if it’s discussing a future event or something that can be explicitly verified within the “natural course of things,” it’s a prediction. If it’s a theory formed around implicit analysis based on evidence and clues, it’s an inference.

Inference: Inference refers to the process of using a trained machine learning algorithm to make a prediction.

Improve Inference latency with Model Pruning If you could rank the neurons or the connection in between them according to how much they contribute, you could then remove the low ranking neurons or connections from the network, resulting in a smaller and faster network.

The network latency is one of the more crucial aspects of deploying a deep network into a production environment. Most real-world applications require blazingly fast inference time, varying anywhere from a few milliseconds to one second. We then share code samples for measuring time correctly on a GPU.

Amazon Elastic Inference performance A dedicated GPU instance will still deliver better inference performance vs EI, but if the extra performance doesn’t improve your customer experience, with EI you will stay under the target latency SLA, deliver good customer experience, and save on overall deployment costs.

Deep learning inference is the process of using a trained DNN model to make predictions against previously unseen data. As explained above, the DL training process actually involves inference, because each time an image is fed into the DNN during training, the DNN attempts to classify it.

Inference applies knowledge from a trained neural network model and a uses it to infer a result. So, when a new unknown data set is input through a trained neural network, it outputs a prediction based on predictive accuracy of the neural network.

An inference pipeline is a Amazon SageMaker model that is composed of a linear sequence of two to five containers that process requests for inferences on data. You can use an inference pipeline to combine preprocessing, predictions, and post-processing data science tasks. Inference pipelines are fully managed.

Inference-as-a-service: A situation inference service for context-aware computing. Abstract: Context-aware computing is to provide situation-specific services, the situation is inferred from the available contexts, and the contexts are acquired from various sources such as sensors, environments, and SNS contents.