When building large-scale applications, multiple components are often involved, such as the front-end, database, APIs, and the machine learning model itself if it’s an algorithm product.
Key concepts like caching, load balancing, the CAP theorem, scalability, etc., must be considered to build the best system possible for the particular scenario.
System design is important for data scientists because it helps us understand how the model will be used in production and ensures we build it in the…