Integrating Machine Learning into Existing Software Systems

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An increasing number of companies are embracing adopting and integrating AI technologies into their products, services, and operations. Machine Learning (ML) systems bring special value to organizations, helping drive personalization, and improve process efficiency, and automation. Since most AI solutions in the corporate world are to some degree ML-based (be it for making predictions, performing data segmentation, and so on), integrating ML models into their existing systems is an important process they must cope with. Nonetheless, the integration process raises some challenges like ensuring compatibility with legacy systems, scaling them, addressing the integration cost, and managing the data consumed by ML systems.

This article provides some hints and guidelines for navigating the process of integrating ML systems into larger existing software systems in an organization.

Key Integration Concepts

 
Some important concepts and paradigms to familiarize yourself with before integrating ML models in existing systems or platforms are explained below:

• Microservices and Container-Based Architectures: Microservices are small, independent services focused on specific functionalities, that are placed together to constitute a larger application. Microservices facilitate ML model integration as individual services, which improves their scalability and maintenance. Containers (such as Docker and Kubernetes) are a method to encapsulate software so that everything it needs to run is packaged into a file system, ensuring consistency across environments. The joint use of microservices and containers facilitates the integration of ML models because containers allow models to be packaged with all their dependencies, making them portable and deployable across different platforms.
• APIs and Restful Services: Once deployed, ML models can be made accessible and callable through APIs. One example is using a REST API to expose the model’s functionality, allowing external applications to send HTTP requests and receive predictions. This is a convenient way to use ML models as standalone services, promoting modular and flexible solutions within the overall software architecture where they are integrated.
• ML Operations (MLOps): The concept of MLOps combines ML development and IT operations to streamline the deployment, monitoring, and maintenance of ML models in production. Similar to DevOps, MLOps facilitates continuous integration and continuous deployment (CI/CD) of models, with a focus on automating workflows and managing the full ML lifecycle. This includes data preparation, model training, validation, deployment, and continuous monitoring once deployed, enabling organizations to keep models updated and aligned with constantly evolving data and business requirements.

Popular Tools for ML Model Integration

ML Frameworks and Libraries

• Tensorflow and PyTorch: popular libraries for developing and training ML models. TensorFlow also provides options for deploying models, exposing them as services.
• Scikit-learn: an excellent option when your ML models are more lightweight, due to its ease of use and compatibility with multiple platforms.

Containerization and Orchestration Tools

• Docker: helps easily package models and their dependencies into portable containers.
• Kubernetes: this tool is particularly useful for scalable orchestration between multiple containers, helping manage resources in production-ready systems.

MLOps Platforms

• Kubeflow: Kubernetes-based tool that allows to manage the entire ML model lifecycle, from development to deployment and maintenance.
• MLflow: a platform specialized in helping manage ML experiments, registering models, and facilitating their deployment.

Cloud Services for ML Integration

The three cloud giants have their integrated solutions to perform training, deployment, and monitoring of ML models, all in one place. AWS SageMaker, Google AI Platform-Vertex AI, and Azure ML, are the most salient tools within these cloud ecosystems. They also support integration with other services in the cloud, for instance, the rest of services an organization may have deployed in a cloud environment.

Case Study: ML Model Integration in an E-Commerce System

 
An ML model can be integrated into an e-commerce platform to enhance product recommendations based on user preferences and behavior. The ML model analyzes user ratings, purchasing history, and patterns to provide personalized product suggestions, significantly improving the customer experience and increasing sales conversions. To achieve its integration, a microservices architecture is considered, thereby deploying the recommender engine as a standalone service accessible via RESTful APIs. This approach enabled seamless updates to the model without disrupting the existing system, ultimately leading to a notable increase in user engagement and revenue. Docker containers are utilized to package the model and its dependencies, and Kubernetes is also used to manage the orchestration, ensuring scalability and efficient allocation of resources. The model was implemented and trained using TensorFlow, to further facilitate the integration process.

Challenges and Wrap Up

 
Integrating ML models into existing software systems brings several challenges, with compatibility issues being one of the most common. These challenges particularly arise when trying to integrate an ML model into legacy systems, requiring the use of middleware or APIs to bridge the gap between them.

Meanwhile, ensuring data privacy and regulatory compliance can further hinder the use of integrated models, since real-world data will be handled differently from the training and test data used in earlier ML development stages. Monitoring deployed ML models is another critical issue, as changes in the application domain often lead to data drifts, requiring regular updates and model re-training to ensure their accuracy and effectiveness remain.
 
 

Iván Palomares Carrascosa is a leader, writer, speaker, and adviser in AI, machine learning, deep learning & LLMs. He trains and guides others in harnessing AI in the real world.