Journey to Full-Stack Data Scientist: Model Deployment
Growing Responsibilities of Data Scientists
The title of data scientist is ever-changing and often vague. It usually involves one who is fluent in mathematics, programming, and machine learning. They spend time cleaning data, building models, fine-tuning, and conducting experimentation. They must also have great communication skills, a good grasp on their domain, and other soft skills.
However, this is not always exactly the case. If you spend enough time scrolling through job boards, "Data Scientist" can differ quite a bit. Some read more like a data engineer, focusing on pipelines and big data platforms. Some are closer to a data analyst, focusing on data cleaning and dashboarding. And as of late, there are many that are similar to software or ML engineering, focusing on object-oriented programming, building applications, deploying models, and sometimes even web development.
And there are those who expect all of this and more, thus, the "full-stack Data Scientist". With this in mind, data scientists should consider looking to go beyond developing models in a notebook and expand their skillset to other areas like ML Ops. As Pau Labarta Bajo says: "ML models inside Jupyter notebooks have a business value of $
