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.
Conducting innovative research in AI is not straightforward. Researchers should focus on a problem area that they are deeply passionate about, e.g., NLP, multi-modal AI, computer vision, speech, synthetic data, graph-based models etc. For research, the problem could be in the theoretical realm. However, if the problem area is grounded in the real-world, then practitioners can actually test their algorithms on real-world data and learn from the feedback.
The most important skill for doing novel research is to think deeply about a particular problem, and apply the scientific method systematically. This involves coming up with relevant hypotheses and conducting several experiments using the right datasets, algorithms, models etc. to test the validity of the hypotheses. An empiricial, data-driven strategy coupled with creative ideas usually leads to novel research output.
To come up with innovative ideas, you need to know the existing literature and what ideas have previously worked or not worked for a particular problem. Sometimes, it is sufficient to translate existing ideas for your particular use cases as well. Knowing what ideas can generalize and are practically feasible to solve a business problem is a rare skill that distinguishes the best applied researchers from the rest.
To become an expert in any discipline, it is important to build a solid knowledge base which can take a significant amount of time. If you build this foundation earlier than others, then you can advance on the journey faster and develop better first-principles thinking and intuition for a variety of machine learning problems. This is particularly true in the case of AI, which requires strong fundamentals in diverse topics including statistics, mathematics, programming, data analysis, presentation, and communication skills.
However, regardless of how early or late you start your career in data science, the key is to keep practicing and honing your skills, given that the field of data science is going to continue evolving rapidly. I have worked with both Bachelors’ students as well as senior IT professionals in their 30s and 40s who are equally motivated to launch their careers in data science.
Given the lack of formal degree education in data science, every data scientist I know is self-taught to an extent. With so many open-source resources, courses, code repositories and datasets available online, any ambitious and motivated person can become a good data scientist. However, to truly become a versatile data scientist, one needs to complement their learning with training from experienced industry mentors and develop a deep understanding of business domains like e-commerce, healthcare, fintech and how data science practically works in industry.
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