Machine learning model inference is the use of a machine learning model to process live input data to produce an output. It occurs during the machine learning deployment phase of the machine learning model pipeline, after the model has been successfully trained. Machine learning model inference can be understood as making a model operational, or moving it into production. It required a fully trained machine learning model to be ready.
The machine learning model lifecycle is a series of phases which takes the model from training and optimisation, to deployment and ongoing monitoring and improvement. Machine learning training is the first phase of such a lifecycle. It includes the collection and preparation of the data and the selection of the type of machine learning model to be trained. It also includes the training and optimisation of the machine learning model to perform the specific task. The phase also includes validation of the trained machine learning model on testing datasets to ensure accuracy and ability to generalise.
Machine learning model inference on the other hand is part of the second phase of the machine learning lifecycle. It is the point the model is put into production, processing live, unseen data. Although the model training phase will be the domain of data scientists, machine learning model inference usually draws from other specialisms such as data engineers and IT specialists. This is because model inference and deployment requires the consideration of wider system architecture where the model will be hosted and the overall flow of input and output data.
Although machine learning model inference and training are different, as part of the overall model lifecycle both phases are important to a functioning model. A holistic view of the entire end-to-end machine learning process is the best way to achieve an accurate model in an efficient way. So understanding the differences between the training phase and model inference is important.
This guide explores the topic of machine learning model inference, including what it is, the main challenges, and comparisons with the training phase.
What is inference in machine learning
Machine learning model inference is the process of deploying a machine learning model to a production environment to infer a result from input data. At this point, the model will be processing new and unseen input data. When a model performs inference, it is producing a result based on the trained algorithm. This means model inference is within the deployment phase of a machine learning lifecycle. The results that are inferred are usually observed and continuously monitored, at which point the model can be retrained or optimised as a separate phase of a model lifecycle.
Machine learning model inference is often referred to as moving a model into production. As such, it’s the use of the model for the task it was originally designed to do. It’s the point that a machine learning model will start to generate a return on the overall project investment. This could be any number of business tasks and processes that models are used to automate or improve, whether classification or regression tasks. The main challenges faced by the model inference process is embedding the model itself within the wider system architecture.
The main considerations for model inference in a live environment include:
- The data flow of both input data from the live environment and the output data for results. This includes the entry point for live data into the model pipeline.
- The system architecture and how the model embeds within it. This could be containerised machine learning models which draw from different system resources, or a server-based pipeline.
- Transforming input data from the live environment into data that can be processed by the model. This may include a pre-processing stage.
- Transforming the output data or result into information that’s interpretable by the organisation. For example a numerical result from a model designed to detect fraudulent activity may need to be transformed into defined labels which can be understood by the organisation.
Machine learning model inference vs training
Machine learning model inference and training can be understood as belonging to different phases of the machine learning model lifecycle. Although they’re part of the same overall end-to-end process, their positions in the process are distinct. Machine learning training is the creation and optimisation of a model using training data. The aim is to reduce the loss function until the model is performing a specific task to a high degree of accuracy. There is a risk that the model may be overfit to the training data, so the model is usually validated against a testing set of unseen data. The process of cross validation in machine learning ensures the model is accurate with new and unseen data, or its ability to generalise. Models will then undergo a process of machine learning optimisation to further improve the model’s accuracy through tweaking of the hyperparameters.
Machine learning model inference on the other hand happens later on in the machine learning lifecycle. It is part of the second major phase in a machine learning model’s life cycle: model deployment. Inference is the application of the trained machine learning model on new data to create a result. Machine learning model inference is also known as moving the model into the production environment. This is the point that the model is performing the task it was designed to do in the live business environment. Deploying the machine learning model includes moving the model to a live environment where the model starts processing new and unseen data. This is different to the model training phase, which is usually performed in a local or offline environment.
Machine learning model inference will usually be managed by a different team to those that trained the model. The training phase of the machine learning process is usually performed by data scientists, whose specialist knowledge is needed to prepare the data as well as train and evaluate the model. The actual deployment of the machine learning model requires different specialist skills, such as IT specialists and data engineers. This is because the model must be embedded within the wider system architecture. Considerations include data pipelines moving input and output data into the wider organisation, and resource management for when and if the model requires scaling.
The training phase will often use an entirely different programming language compared to the language that the deployment team is used to. Taking a holistic view of the two phases and providing a common framework is an important consideration, as is an awareness of the differences. Increasingly, the machine learning lifecycle is managed using MLOps, which mixes the best practice elements of DevOps with machine learning. Machine learning model inference sits within the deployment phase of any MLOps pipeline.
The training and model inference may seem similar, as in both cases data is fed through the model. However in training, a model will process the data many different times, reducing the loss function iteratively. Outputs are evaluated and re-evaluated, often against known results. In the case of artificial neural network models, each iteration will adjust the weighting of data within the separate layers of nodes. Training data is also usually processed and labelled in the case of supervised machine learning techniques. In contrast, model inference is the process of using a trained model to infer a result from live data. Although the results might be monitored for future optimisation, model inference is simply the processing of unseen data using the trained model to produce a result.
Challenges for machine learning model inference
The training of a model is often the first thing that comes to mind when thinking of the challenges of machine learning. The training phase is a specialist domain, in which data scientists must collect and prepare training data, before designing and training complex models to perform a task. Even the pre-processing of the training data is complex, as labelled training data used in supervised machine learning models takes time and resources to prepare. But machine learning model inference has its own set of unique problems, dealing with the challenge of moving a model into production.
The main challenges that must be overcome to achieve successful machine learning model inference include:
- Embedding a machine learning model within the organisation’s wider system architecture can be a major challenge. This includes ensuring the system resources are available for a model that can be scaled, and the flow of data in and out of the model.
- Data may need to be transformed from a format that can be processed by the model, to one that can be interpreted by the organisation. This means a model inference pipeline may require both pre and post-processing of data.
- Different skill sets are required for successful model training and model inference. Data scientists are usually required for the data preparation, training and retraining of models, but data engineers and IT specialists have the skills for moving models into production.
- The model may be written in a different language to one that IT specialists in the organisation are used to.
- It can be a challenge to seamlessly integrate model inference and deployment into the overall machine learning lifecycle because of the different skill sets and teams. An end-to-end plan or lifecycle pipeline is the best way to map these different phases.
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