How does the Future of AI look like?
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.
If you have a quantitative background in computer science, engineering, physics, finance, and related disciplines, you already have the core technical skill set to transition and excel in data science.
Candidates from a non-technical domain, on the other hand, have the advantage of domain knowledge. Doing well in data science requires a deep understanding of both the data (and the business domain) as well as the scientific aspects of analyzing data. I have seen and coached several candidates from non-traditional backgrounds in transitioning to data science and becoming successful practitioners and experts in the field.
My general advice to candidates interested in data science is to realize that they might already have several skills relevant to the data science industry. You only need to bridge the gap in the skills you lack or are less confident in to crack jobs at top tech companies and startups successfully.
Machine Learning Engineering is a relatively new role in the data science job family. The ML engineer role is in high demand as organisations have realised that they cannot realise their business goals without first deploying machine learning models to production.
In large organisations with big machine learning teams, Machine learning engineer is a specialised role that is distinct from the role of a data engineer or a data scientist. In a previous article, I have compared the roles of a data scientist vs. machine learning engineer in detail.
As machine learning becomes an increasingly engineering focused discipline, there is a massive requirement for professionals who combine strong software engineering skills with an understanding of the entire data science lifecycle from raw data through to model production, maintenance and monitoring. Machine learning engineers typically focus more on building pipelines and infrastructure to ensure that all the MLOps are running smoothly without any failures.
I have shared a curated list of top 10 resources to help become a Machine learning engineer. Candidates can dive deeper into these courses, books, papers, and blogs to prepare for machine learning engineer job interviews.
Here are some blogs that help demystify the hiring process for ML roles:
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