What is model deployment in machine learning?

Model deployment is the key process to apply the trained machine learning model to the actual production environment. In this environment, models can process input data and generate corresponding outputs. The purpose is to make it easy for others to leverage the trained model to make predictions.

Many online resources focus on early stages of the machine learning life cycle, such as exploratory data analysis (EDA), model selection, and evaluation. However, model deployment is often overlooked because it involves complex processes. Understanding the deployment process can be difficult for people without a background in software engineering or DevOps. Therefore, despite being a crucial step in machine learning, deployment is rarely discussed in depth.

This article will introduce the concept of model deployment, explore the high-level architecture of the model, and different deployment methods. It also discusses factors to consider when determining your deployment approach.

What is model deployment?

Deploying a machine learning model is the process of applying a trained model to a real production environment. Through deployment, the model can receive input data and generate predictions, making it easy for users, managers, or other systems to use machine learning models for predictive analysis. The main purpose of deploying a model is to ensure that the model can run effectively and provide accurate prediction results in practical applications.

Model deployment is closely related to machine learning system architecture. Machine learning system architecture refers to the layout and interaction of software components in the system to achieve preset goals.

Model Deployment Standards

Before deploying a model, a machine learning model needs to meet several criteria to be ready for deployment:

• Portability: This refers to the ability of software to be transferred from one machine or system to another. A portable model is one that has a relatively short response time and can be easily rewritten.
• Scalability: This refers to the scale to which the model can be expanded. A scalable model is one that maintains its performance without redesign.

In actual application, all these operations will be completed in the production environment. A production environment is the environment where software and other products actually run and are used by end users.

Machine learning system architecture for model deployment

At a high level, a machine learning system has four main parts:

• Data layer: The data layer provides access to all data sources required by the model.
• Feature layer: The feature layer is responsible for generating feature data in a transparent, scalable and usable way.
• Scoring layer: The scoring layer converts features into predictions. Scikit-Learn is the most commonly used and is the industry standard for scoring.
• Evaluation layer: The evaluation layer checks the equivalence of two models and can be used to monitor production models. It is used to monitor and compare how well training predictions match real-time traffic predictions.

3 Model Deployment Methods You Need to Know

There are three common methods to deploy ML models: one-time, batch and real-time.

1. One-time

It is not always necessary to continuously train the machine learning model for deployment. Sometimes, a model is needed only once or periodically. In this case, the model can simply be trained ad hoc when needed and then put into production until its performance deteriorates enough that it needs to be fixed.

2. Batch

Batch training can continuously have the latest version of the model. This is a scalable approach that takes a subsample of the data at a time, eliminating the need to use the full dataset for every update. This is a good approach if you are using the model on a consistent basis but don't necessarily need real-time predictions.

3. Real-time

In some cases, real-time prediction is required, such as determining whether a transaction is fraudulent. This can be achieved by using online machine learning models such as linear regression using stochastic gradient descent.

4 Model Deployment Factors to Consider

There are many factors and influences that should be considered when deciding how to deploy a machine learning model. These factors include the following:

• How often predictions are generated and how urgently the predicted results are needed.
• Should predictions be generated individually or in batches.
• The latency requirements of the model, the computing power it has, and the required service level agreement (SLA).
• The operational impact and costs required to deploy and maintain the model.

Understanding these factors can help you choose between one-time, batch, and real-time model deployment methods.

The above is the detailed content of What is model deployment in machine learning?. For more information, please follow other related articles on the PHP Chinese website!