MLOps platform for scaling deep learning model training and inferencing
Challenge
The client faced the challenge of scaling deep learning models to efficiently manage high traffic and large image datasets. They required a solution that would simplify access to these models, allowing data scientists to rapidly test and deploy them while avoiding the complexities of infrastructure management.
Our approach
Our team undertook an MLOps project focused on deploying a web application on Azure Kubernetes Service (AKS) to democratize deep learning model access. Leveraging Azure's powerful infrastructure, we designed a solution that provided scalability, cost-efficiency, and ease of use for data scientists.
Key components of the solution included:
- Automated CI/CD processes adhering to organizational standards and best practices.
- Horizontal and vertical resource autoscaling to optimize worker and GPU utilization.
- Model versioning, monitoring, and retraining pipelines to ensure models adapt to data and concept drift.
- Containerization of trained ML models as microservices to optimize model inference for both online and batch predictions.
The outcome
Deploying the web application on AKS delivered significant benefits, including cost-effective scalability and efficient handling of high traffic and large datasets. Customizing Kubernetes autoscaling provided a more affordable and flexible alternative to Azure Machine Learning (AML) managed endpoints. Optimizing GPU usage further minimized costs by reducing underutilization through memory sharing among workers.
Additionally, implementing CI/CD pipelines with GitHub Actions enabled seamless testing, validation, and deployment, empowering the client to iterate quickly and deliver enhanced value to their users.