How do I prepare for Machine Learning Engineer job interviews?

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.

1. Stanford: ML Systems Design [course]
2. Educative: ML System design [course]
3. Made with ML: MLOps [course]
4. Designing ML Systems [book]
5. Practical MLOps [book]
6. Google Practitioner's guide to MLOps [white paper] 
7. Google ML Engineering Best Practices [blog]
8. ​Microsoft Data Science life cycle [blog]
9. ML Engineer vs Data Scientist [blog]
10. ML Product Development [video]

Here are some blogs that help demystify the hiring process for ML roles:

• Machine Learning hiring guide 
• How to Hire Data Science Teams
• ML Engineer vs Data Scientist
• How to build ML Teams that Deliver?