Microservices-based Architecture: Key to Scaling Enterprise ML Models
The last decade has seen wholesome innovations in the field of artificial intelligence and machine learning (AI/ML). Today, progressive enterprises are increasingly leveraging ML models to drive operational growth through actionable decision-making. In the ML space, an important area of evolution has been MLOps – a set of practices that help companies synergize ML, DevOps, and data engineering to seamlessly deploy ML models and reliably maintain ML systems.
Even when ML has gained significant popularity in helping organizations address operational gaps, there’s still a challenge when it comes to deploying ML models to the production environment. The major obstacle that companies face here is the constantly changing training data sets. Using prebuilt offline models to develop practical applications can often turn out to be a tricky affair as organizational data is always evolving. In order to add new data sets to an existing application, data teams need to rebuild data models from the scratch, freshly compute scores and deploy new models without impacting the production environment. It is here where a microservices architecture can serve as a natural solution enabler helping companies achieve seamless data integration without disrupting the existing production environment.
Microservices: Helping Companies Efficiently Deploy, Productionize, and Scale ML Models
Research reveals that microservices have seen continued adoption, and the market is expected to grow exponentially over the next decade. The global market of microservices architecture is projected to be worth $8.07 billion by 2026, up from $2.07 in 2018, growing at a healthy CAGR of 18.6%. With both the microservices architecture and MLOps on ascendancy, combining the concepts promise to bring ML success to enterprises that are envisioning a future that relies on fast application deployment, seamless operations, and agile development.
The architecture to develop and deploy machine learning models has a telling impact on its success. The ML model lifecycle ideally has four key components, which are:
The microservices-based architecture helps create a robust ML model lifecycle, which ensures mitigation of challenges arising from underutilization and mismanagement of data scientists and engineers, fragmented communication, and siloed operations. Also, microservices-based architectures make ML models more intuitive for the users, thereby increasing productivity and improving the customer experience.
Another key element of microservices-driven MLOps is going serverless, which is crucial for automating container development, auto-scaling of workflow, and improved visibility and monitoring. To build automated ML pipelines, implementing serverless frameworks and utilizing an open-source tool, such as Kubeflow, is essential. By using Kubeflow pipelines and serverless frameworks, such as MLRun or Nuclio, enterprises can quickly develop and deploy automated ML pipelines without the need to overtly focus on DevOps.
Productionizing ML models has distinctive benefits, and a microservices-based architecture facilitates a product-oriented development and deployment of the ML model and offers end-to-end visibility of the process. This approach helps in making the development process both interactive and iterative, resulting in creation of robust, automated, CI/CD-based ML pipelines. In other words, ML pipeline architecture and Kubeflow pipeline are crucial for productionizing ML models and unlocking the complete value of MLOps.
The following are some of the best practices when it comes to facilitating successful MLOps through a microservices architecture:
- Development and Deployment: One of the key aspects that define the success of any ML application is how easily the ML models can be developed and deployed. A common approach to ensure that development and deployment of models happen without disrupting the production environment, is to leverage a robust training dataset to create an optimized model offline. This model can then be validated offline against a pre-defined validation dataset and then uploaded to the microservices architecture. Teams can use the main messaging hub in microservices architecture to collate critical feature data from multiple disparate sources on a real-time basis.
- Packaging: Microservices architecture can allow developers to both publish a single ML capability as a standalone microservice or publish an independent application functionality as a microservice with ML capability. In both the cases however, the ML component needs to be managed separately because of complexities associated with processes like data acquisition, model training/development, model update and so on.
- Containerization and Management: In order to integrate the ML algorithm into the delivery IT platform, there are two major steps that needs to be followed. Firstly, REST API has to be integrated with the ML algorithm running behind a particular microservice. The API, run time and algorithm has to be packed in a Docker container. Secondly, all the containers (microservices) will have to be effectively managed and orchestrated. It is here where Kubernetes can help create a difference. Kubernetes can help teams properly categorize containers into compute clusters and ensure that workloads are managed as planned.
- Scaling and Performance: Since cloud and containers serve as the backbone of microservices, scalability and performance become an integral aspect. While selecting and developing different application components, developers should carefully select and finalize databases, runtimes, computing engines and storage on the basis of desired performance and scalability.