Inference is a logical operation based on induction. Inference in machine learning and deep learning aims to make efficient predictions from a trained learning model.

What is machine learning inference?

In machine learning (or automated learning), the inference phase refers to the execution of an AI model once it has been trained on a learning data set and then tested on a validation data set. It therefore refers to the deployment of the model, and the application of its scoring in a real production situation based on field data.

The inference of an artificial intelligence model is generally implemented by a data engineer or a DevOps engineer. In some cases, the data scientist responsible for its configuration and training is responsible for deploying it himself. But entrusting him with this task is not always relevant insofar as he is not necessarily equipped in terms of skills to carry it out.

How does machine learning inference work?

During the inference (or deployment) of a machine learning model, the latter will ingest captured field data and then process it to achieve the expected result. Take the example of a CCTV AI. Upstream, the video image is captured in real time by a network of cameras. The flow is analyzed by an image recognition model which will have been previously trained to recognize suspicious movements (running, crowd movements, etc.). If necessary, an alert is sent to the security PC connected to the camera network.

Able to be executed both at the edge of the network (edge computing) and on a centralized cloud, inference can be applied both to a statistical machine learning model and to an artificial neural network oriented deep learning (deep learning).

What is the architecture of a machine learning inference system?

Once put into production, a machine learning model can process data from an application as well as a connected object or even a point-of-sale terminal. This information is then federated in real time (or streamed) within an Apache Kafka-type platform on which the learning model is applied.

If the purpose of the model is to calculate a fraud score on purchase data, for example, a refusal or approval message will then be automatically generated depending on the case in the associated business applications.

What are the main types of machine learning-oriented inference?

There are two main categories of inference (or deployment modes) of machine learning models which will be applied according to the needs in terms of responsiveness but also with regard to technical considerations (volumetry, latency, etc.):

  • Batch inference: predictions are processed regularly from observations grouped into batches (batch mode).
  • Real-time inference (or streaming): predictions are made in real time and thus provide immediate answers.
  • Depending on the evolution of the context, the model can also be retrained from a new learning data set. A process that is all the more important as the environment is changing.

How to successfully infer a machine learning model?

The success of the deployment of a machine learning model will often depend on the coordination between the teams of data scientists, in charge of its development, and the teams of data engineers, whose mission is to put it into production.

Faced with this challenge, the implementation of an MLOps approach is strongly recommended. This approach aims to design machine learning models that are suitable for deployment in production. Like DevOps for applications, it also aims to manage the entire life cycle of models.


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