Designing ML Systems - ML overview | Machine Learning System Designs

As ML applications are maturing over time and becoming an indispensable component of industries for making faster and accurate decisions for critical and high-value transactions.

For example,

1. Recommendation system to increase click-through rate for e-commerce.

2. Increasing engagement time users for social media apps.

3. Applications in the medical field and self-driving cars.


In all the above scenarios, the expected ML response is accurate, fast, and reliable. Scalability, maintainability, and adaptability also become critical as we move towards making ML one of the main components of enterprise-level applications. Hence, designing of end to end system keeping requirements of ML becomes important.

Machine Learning System Design

System design for machine learning refers to the process of designing the architecture and infrastructure necessary to support the development and deployment of machine learning models. It involves designing the overall system that incorporates data collection, preprocessing, model training, evaluation, and inference.

The process of defining an interface, algorithm, data infrastructure, and hardware for ML Learning system to meet specific requirements of reliability, scalability, maintainability, and adaptability.