Leveraging microservice architecture for agile AI solutions in enterprises

What is microservice architecture?

Microservice architecture is an approach to service development where a single application comprises many loosely coupled and independently deployable services. Each microservice focuses on a specific set of features and can be developed, deployed, and scaled independently. This can be compared to the traditional, or monolithic, approach, in which all services are closely connected. Sounds confusing? Well, it’s not as complicated as it might sound. Let’s break it down.

Imagine you own a bookstore. Think of this bookstore as your application. In your store, you have different departments, such as Fiction, Non-fiction and Children's books, and perhaps even a coffee shop. These are your microservices. If your bookstore followed the traditional approach (monolithic architecture), everything would be tightly connected. All departments (microservices) would have to coordinate and make decisions together. If the Fiction department wanted to change its book arrangement, they would need to consult with everyone else, including the coffee shop. I think we can all imagine the frustration the person in charge of the Fiction department would feel having to consult the coffee shop owner about their book selection. It would likely slow down the decision-making process and make it harder to manage changes.

In contrast, with microservice architecture as an approach, each department (microservice) of your bookshop (application) would operate independently, without being slowed down by others. For example, if the coffee shop becomes popular, you can expand it without disrupting the Fiction or Children's books departments. Similarly, if you need to temporarily close the coffee shop down to refurbish it, you can do so without having to close down the other bookshop departments. 

As demonstrated by our bookshop example, there are many benefits of applying microservice architecture in enterprise ecosystems. Benefits include:

• Scalability: Individual services can be scaled independently based on demand.
• Flexibility: Different technologies can be used for different services.
• Resilience: Faults are isolated to individual services, enhancing overall system reliability.
• Continuous deployment: It facilitates frequent updates and continuous delivery of new features

In other words, with microservice architecture, the whole system is more flexible, scalable, and resilient.

Monolithic versus microservice architecture, in short:

• Monolithic architecture: A single, unified codebase where all components are interconnected and interdependent. If one fails, everything fails.
• Microservice architecture: A collection of small, autonomous services, each running in its own process and communicating through lightweight mechanisms. Issues in one part of the ecosystem do not necessarily affect the others.

How microservice architecture can be used to deploy AI solutions

So, we now know what microservice architecture is and how it can benefit an enterprise ecosystem. But how does it relate to AI? Well, it offers the perfect approach for businesses to safely deploy AI models and services and future-proof the applications to which these are deployed. Each AI component, which can be anything from data processing to model training and inference, can be developed as a separate microservice. This allows for independent scaling and updates to the application, enhanced flexibility and maintainability, faster development and deployment cycles of components, and improved fault isolation and system resilience.

Creating AI microservices

At the core of microservices architecture is, of course, agility. Agile AI development emphasises iterative progress, collaboration, and flexibility. Rather than developing and deploying the entire AI solution at once, it’s developed in small, manageable increments. Feedback is key and is therefore regularly gathered from stakeholders to help refine the different AI models and services. This also requires close collaboration between teams, such as data scientists, developers, and business analysts.

Designing AI solutions as modular services involves:

• Identifying core features and breaking them into distinct microservices
• Ensuring each service has a well-defined scope and interface
• Implementing robust communication protocols between services

Examples of AI microservices:

• Natural language processing (NLP): Services for language translation, sentiment analysis, and chatbots.
• Computer vision: Services for image recognition, object detection, and facial recognition.
• Predictive analytics: Services for forecasting trends, customer behaviour analysis, and risk assessment.

Deployment strategies for AI microservices

As with most things in tech-enabled marketing and communication, there are various strategies you can use to deploy AI microservices:

Containerisation

Involves using containers, such as Docker, to package and deploy microservices. This approach encapsulates the microservice and its dependencies, ensuring consistent and reliable deployment across different environments. Containers provide a lightweight, portable, and efficient solution that simplifies the management of microservices.

Orchestration

Refers to the use of orchestration tools, such as Kubernetes, to manage and scale microservices effectively. Orchestration tools automate the deployment, scaling, and operation of containerised applications, handling complex tasks like load balancing, service discovery, and fault tolerance. Kubernetes, for instance, enables developers to define desired states for their applications, and it continuously works to maintain those states, ensuring high availability and efficient resource usage.

Continuous Integration and Continuous Deployment (CI/CD) pipelines

These are essential for automating the testing and deployment of microservices. CI/CD pipelines integrate various tools and practices to streamline the software development lifecycle, from code commit to production deployment. By automating testing, integration, and deployment processes, CI/CD pipelines reduce human error, accelerate release cycles, and ensure that changes are continuously validated and deployed, resulting in higher quality and more reliable software.

CI/CD pipelines’ main benefits are to automate the process of integrating code changes, running tests, and deploying updates to production. For AI microservices, this involves several steps:

Automating data preprocessing and model training

Let’s revisit our bookstore example. Imagine your bookstore wants to personalise book recommendations for each customer. The first step is collecting and processing customer data, like their purchase history and preferences. In a CI/CD pipeline, this data preprocessing and the training of recommendation models are automated. This ensures that the models are always updated with the latest data without manual intervention.

Running automated tests to validate model performance

Just like you would regularly check the quality of your café's coffee to ensure it keeps upholding its standards, you need to run automated tests of your AI models to validate their performance. If the model's performance dips, it gets flagged for review before being deployed.

Deploying models to production environments seamlessly

Once the model passes all tests, it's ready to be deployed. In your bookstore, this is similar to updating the book recommendation display regularly with the newest and most relevant selections. The CI/CD pipeline ensures that these models, updated with real-time data, are deployed seamlessly.

By automating these processes, CI/CD pipelines help maintain high-quality AI microservices that are continuously improved and quickly updated.

Many are late to the AI party, but there’s still time

Many companies are struggling to keep up with technological advancements, often because of slow decision-making. However, if you’re late to the party, don’t worry. There’s still time, not only to catch up and stay relevant but to gain a competitive edge by swiftly responding to market changes and delivering exceptional value to your customers.

Microservice architecture offers a powerful approach to developing agile AI solutions, enabling enterprises to enhance flexibility, scalability, and resilience. By breaking down AI applications into modular services, businesses can achieve faster development cycles, improved fault tolerance, and continuous delivery of value.

The combination of AI and microservices has the potential to revolutionise enterprise operations, driving innovation and competitiveness. As enterprises continue to adopt these technologies, they can unlock new opportunities and create sustainable growth.

References and Further Reading

• "Building Microservices" by Sam Newman.
• "Continuous Delivery" by Jez Humble and David Farley.
• "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.
• Official SAFe website: scaledagileframework.com