Learn these things to become a more well-rounded data scientist
The role of a data scientist is now changing. Businesses no longer want PoC models in Jupyter notebooks as they provide zero value. That’s why, as data scientists, we should up-skill ourselves in software engineering to better deploy our algorithms. In this article, I want to break down the essential software engineering skills you need to learn as a data scientist.
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 most appropriate way for that system.
We want our model to go into production as smoothly as possible, and understanding the whole architecture helps tremendously with this.