The field of AI has changed dramatically over the last decade. Consequently, the role of a data scientist has also transformed and evolved into multiple specialized roles like data engineer, machine learning engineer, research scientist, applied scientist, AI product manager, and so on. I believe that we are still in the early days of AI, and it is as good a time as ever to break into data science.
Data science is also becoming more engineering-focused as companies realize that business value cannot be realized until a robust infrastructure is in place to deploy, monitor, and maintain data science models in production. As a result, data science offers an opportunity for software engineers to transition laterally and work more closely with data and models apart from code.
Additionally, data science has matured as a field with the advent of several tools and products that make the entire data science life cycle more efficient, transparent, and reproducible. The organizational time, effort, and resources needed from conceptualization to production of machine learning models are reducing, enabling data scientists to drive more significant business impact.
Another trend is the focus on deriving business value from massive amounts of unstructured business data like images, text, audio, and video apart from structured, tabular data. For such applications, deep learning models are particularly relevant. We are currently witnessing a tremendous amount of innovation and advances in this area, with groundbreaking models like BERT, GPT-3, DALL-E 2, Imagen, and Whisper, to name a few.
We will see a more significant business impact of innovative AI R&D where startups and large companies leverage these technologies to build new products and services. It is, therefore, even more exciting to be at the forefront of AI innovation and build a long-term career in data science and AI.
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