Before launching new products, manufacturers will often perform extensive testing to determine whether they would work as intended in real-world scenarios. Similarly, data scientists use specific deployment methods for machine learning models to ensure optimal functionality.
Each method offers different benefits, so it’s important to identify the most popular deployment approaches, the challenges to watch out for during the process, and the tools that are available to help.
How to deploy machine learning models
When deploying a machine learning model, first consider the main objectives of the finished product. What problems will it solve to be a valuable asset for users? How will you measure the success of the solutions it provides? By defining an ideal machine learning model, you can more easily determine the tactics that will enable its full capabilities.
Generally, a machine learning model is considered successful if it can work with necessary datasets, function as intended, and generate beneficial insights for its users. Developers can apply each deployment method to machine learning models to support the success of their intended operations.
Model evaluation in training environments can help developers assess the model in a controlled, offline environment. Training environments can also be used to revise or update versions of existing ML models.
You can compare the live models with models containing new features to see if they perform like the live model. By deploying the new model in a training environment, you can evaluate the new model’s actions with the real-world data and compare its functions without releasing it to the public or interrupting the functionality of the live model.
ML model code testing and cleaning
For an ML model to be successful in a live environment, the code should be tested to assure its quality. Once the developers have agreed on the intended results of the model, they can clean the code and test the model in a training environment to check that it executes its functions properly. This involves identifying bottlenecks and determining the steps necessary to reach optimal performance before deployment.
Another common method is using containers as the ML deployment environment. ML deployment with containerized code has several advantages, like easy scalability, faster development, and sufficient maintenance. This is because containers ensure a consistent environment, so developers can apply changes to distinct areas of the model.
Post-deployment monitoring and maintenance
Establishing ways to monitor the machine learning model after it’s been deployed will make it easy to identify and resolve data drift, outliers, and other emerging inefficiencies. Regularly retraining ML models with new data is one way to avoid model data drifts. This ongoing governance and maintenance will ensure that the machine learning model continues functioning as intended in the long term.
Learn more on eWeek: How to Accelerate Machine Learning Model Development in the Enterprise
Challenges to consider when deploying machine learning models
Some of the main challenges when deploying machine learning models are data variance and integrity. For a model to generate valuable insights for the user, it must be able to access and analyze datasets correctly.
Data must be cleaned and utilized consistently from one testing process to the next. Otherwise, variations can change the model’s output and behavior once deployed. In addition, the data that the model utilizes may change over time. This is why post-deployment maintenance is crucial to ensure that no data changes can cause problems that could hinder the model’s functionality.
Drifts caused by changes in the concepts or data can affect the desired goal of the model and result in the original ML model function losing relevance and usefulness. For example, if the training data becomes less relevant to the user’s current needs due to a data drift, the model’s results will be less helpful. Likewise, a concept drift can make the model’s predictions less accurate if the user’s concept of the model’s ideal functionality changes.
Read more on TechRepublic: 4 ways to fine-tune your AI and machine learning deployments
Tools that can help with deploying ML models
Some tools like MLBox, RapidMiner, AWS Sagemaker, Cortex, and Kubernetes can help with deploying ML models effectively.
MLBox is a machine learning automation tool that can be helpful for ML model testing and quality assurance. The preprocessing feature reads and cleans data and can create datasets for training and testing. MLBox also has capabilities for avoiding typical ML model hiccups like data drift thresholding and encoding missing values.
RapidMiner is a data science platform for visual workflow design. Developers can use the tool to support their ML development processes like data preprocessing, visualization, loading, and transformation. It has code-based, visual, and automated data science features, so users with and without coding experience can utilize the tool. It also enables users to apply various assessment techniques to test ML models, like cross and split validation.
AWS SageMaker is a cloud-based platform that enables developers to create, train, and deploy their models by simplifying various parts of the machine learning process. It provides users with many data-related capabilities like accessing data from structured and unstructured sources and preparing it to ensure the accuracy of ML models. Users also use SageMaker MLOps tools to develop, train, test, troubleshoot, deploy, and manage their models at scale.
The Cortex open-source tool can help developers with serving models and other management operations like model monitoring. With features like microservices scaling, data processing pipelines, and other MLOps capabilities, users can deploy scalable and effective machine learning models and maintain their accuracy as time goes on.
Kubernetes is a container orchestration platform that can automate various aspects of container management and application deployment. Its capabilities make it an optimal tool for containerized model preparation, including batch execution to replace containers that fail and the self-healing feature for automated pod management.
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