Machine Learning Model Deployment: A Guide

Understanding Machine Learning Model Deployment

Let us assume that you're working as a data scientist for an advertising firm. Your business problem is to build a user segmentation model that will help boost sales. You begin by breaking down the business problem (or requirement) into actionable and implementable steps. Next, you take a deeper look into the data, performing exploratory data analysis (EDA). This is followed by data preparation, feature engineering, and model development. Finally, you tune any model hyperparameters and evaluate the performance of your model on a validation (or test) set. The model is steady and results in high-performance scores.

Model development occurs locally in an offline mode. It is a trial-and-error process, done iteratively. It's a proof of concept that works on past data. However, the model still needs to solve the said business problem.

Model deployment is a term used to refer to the phase of machine learning wherein the model is used in real-time, collecting and processing incoming data, to meet the business requirement (in this case, boost company sales).

What does machine learning model deployment entail? It involves integrating the development environment (the stack/tools/programming languages used while developing the machine learning model) with the deployment environment. By integrating the model into the production (deployment) environment, the company can finally make actionable business decisions. As you'll learn, there are several challenges associated with smooth integration.

In the following section, you'll learn about the steps involved in deploying a machine-learning model.

Steps to Deploy a Machine Learning Model

Model Development and Training

As discussed earlier, model development entails training a machine-learning model on existing data. This is done by data scientists in the development environment. Model development is a repetitive process in which a team of data scientists tests several algorithms, featuring engineering techniques to build a robust and accurate model.

The development environment is equipped with the necessary tools/software for data storage and programming. In addition to that, the environment must meet hardware requirements, such as GPU/CPU requirements for execution and computation.

Validate (Test) Machine Learning Model

A standard way to determine whether a model is overfitting on the training data is to perform model validation. Model testing/validation is done in the development environment.

In this process, new or unseen data is fed to the model. This puts the model to the test and helps evaluate how well the model might do in production. Several models are validated using several different hyperparameters. The model resulting in the highest accuracy or score is selected for model deployment.

In addition to that, data scientists must rigorously clean, test, and document the code several times before passing it to the production team. This enables collaboration between the development and production teams. Collaboration is key to establishing an agile workflow.

Model Deployment

Deploying a model encompasses the idea of integrating the model into the production environment. This environment contains data scientists and MLOps engineers who work in collaboration with the developers. At this end of the organization, the operations team determines (1) how to extract/process unstructured data in real-time, (2) how much storage is required, (3) how to store/collect model (and data) artifacts/predictions, (4) how to set up APIs, tools, and software environments (5) what the time period of predictions (instantaneous or batch predictions) should be, (6) how to set up hardware requirements (or cloud requirements for on-cloud environments) in accordance with the computational resources required, and (7) how to set up a pipeline for continuous training and parameter tuning. Model deployment is a challenging task, with several moving pieces, tools, and teams. It is crucial to establish a deployment strategy beforehand.

Monitoring the Model

Finally, monitoring and maintaining the machine learning model is the last step of the cycle. This process entails constantly auditing model artifacts and reiterating to check if the model is making reasonable predictions.

The key idea here is to identify, assess, and manage any issues post-model deployment.

Challenges of Machine Learning Model Deployment

• Only 13% of machine learning models make it to production. This is partly because some organizations lack a strategic plan for model deployment and maintenance. Approaching model deployment as it comes (without a plan) is not a good idea.

• There are several factors to consider while deploying a model—for instance, integration into the production environment, amount (and cost) of resources, team collaboration, functional tools/software, model monitoring, infrastructure scaling, automation, and more. This makes deployment an expensive and convoluted task. Moreover, due to the constant influx of new data, machine learning models are highly dynamic in nature and must be continuously trained and tuned. This adds to the complexity of automating model deployment.

• Similar to DevOps, machine learning operations (MLOps) is a set of practices and a framework that allows teams to efficiently integrate the development and deployment (as well as maintenance) of machine learning models. MLOps enables building robust machine learning pipelines at a large scale, automating the process of model deployment. Furthermore, it also fosters a culture of collaboration and clear communication between data scientists and operations engineers. However, organizations are new to the idea of MLOps and find it difficult to adapt to it at large.

• Software development environments/stacks vary across machine learning environments. Thus, as organizations make a shift to become more data-driven, they must set up new environments for both the data science and operations teams. Several changes within the teams (such as hiring more data scientists and data engineers) must also occur in order to establish a framework for machine learning deployment.

How to Improve Machine Learning Model Deployment

• Set up an open collaboration and communication pipeline between data scientists and the operations team. Hire MLOps engineers who see through the integration between the two teams.

• Invest or set up a unified platform that allows your teams to track, manage, and monitor machine learning models, as well as data

• Actively log model and data artifacts by using versioning tools. Continuously measure performance metrics throughout the training and validation period.

• Use tools or write scripts to automate data cleaning and preparation. Additionally, set up model triggers. For instance, each time more data enters the pipeline, a trigger must activate continuous model training.

• ML models are computationally expensive and require a lot of resources and continuous scaling. Several businesses offer infrastructure for the development and deployment of ML models. Put thought into infrastructure requirements. Weigh the pros and cons of outsourcing vs. on-premises infrastructure to see what fits your needs.

Plutora offers a powerful platform to perform analytics and AI. They integrate the development and deployment through a unified dashboard that allows organizations to track and monitor their models from one place. Moreover, they use automated tools that can easily integrate into your existing machine-learning pipelines. You can check if they fit your needs by simply signing up for a demo.

With this, we come to the end of the post. I hope you now have a better understanding of machine learning model deployment, the steps, and the process, as well as how to deal with challenges while deploying machine learning models.