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:
How Data Science helps Business?
Data science is now considered an integral function for modern companies. Data science provides companies a massive competitive edge in terms of:
Data-driven companies are able to achieve their goals faster and realise at least 20% more earnings. Proven statistics like this provide a significant impetus for business to invest in building and hiring data science teams that act as the catalyst for bringing a data culture across the entire organization.
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