Becoming an ML (AI) Engineer in 2025 ...
What I'd do, if I had to start again
I was planning to talk about becoming an ML / AI Engineer today, but with DeepSeek out โฆ well, I guess itโs time to close our laptops and explore careers in other fields, right? Programming is done. Developers are obsolete. This is the end.
Just kidding ๐
Todayโs post is about books, blogs and courses - the key resources that shaped my journey from a Physics graduate (who thought Python was just a type of snake ๐คฃ) to my current role as a Senior ML Engineer. Am I also an AI Engineer? Thatโs a discussion for another time.
Iโve categorized these resources into four groups:
๐น Full Courses ๐บ
๐น Blogs ๐ฅ๏ธ
๐น Books ๐
๐น Content Creators ๐ฑ
Ready? Letโs begin!! ๐
๐ Full Courses
Courses have been a part of my journey from day one. Still remember the day I started the Machine Learning specialization on Coursera, by Andrew Ng. Does that make me old under AI standards ๐?
Hereโs my top 3 course recommendations for aspiring Machine Learning Engineers:
๐ธ ๐๐๐๐ก๐ข๐ง๐ ๐๐๐๐ซ๐ง๐ข๐ง๐ ๐ข๐ง ๐๐ซ๐จ๐๐ฎ๐๐ญ๐ข๐จ๐ง - DeepLearning.AI
A fantastic introduction to building ML Production Systems, covering essential topics such as modeling strategies, pipeline monitoring, experiment tracking and data augmentation. A must for MLE beginners.
๐ธ Made with ML - Goku Mohandas
One of my favourites. High quality content that shows you how to combine Machine Learning with Software Engineering best practises. Believe me, thatโs the key behind production-ready ML Systems!
๐ธ The Full Stack
Teaching you how to build ML Systems with a hands on approach. Amazing content, amazing interviews and, if youโre interested in LLMs, you canโt miss the LLM Bootcamp!
๐ Blogs
Now that you know my top course recommendations, letโs take a look at the blogs!
๐ธ ApplyingML
System design, Feature Stores, ML Testing, counterfactual evaluation, real-world systems, explained in detail. What can I say about this blog? Every single article is pure gold.
Also, donโt miss Applied LLMs, which follows the same approach but with a focus on LLMs
๐ธ Chip Huyen Blog
Chip Huyen is not only the author of my favourite ML book, but she also writes an amazing blog. Articles about MLOps, ML Systems, LLM theory, โฆ even how to prepare ML interviews! You should definitely check it out.
๐ธ Eugene Yan Blog
Eugene's blog is one of the gems in the ML Engineering field. He covers many topics, such as LLM patterns, Sytem Design for Recommender Systems, ML Testing, Pipeline Testing, etc.
๐ Books
There are a lot of amazing books in this field, but, if I had to pick the two that truly shaped me - okay, three (though I havenโt finished the last one yet ๐ ) - here they are:
๐ธ Designing Machine Learning Systems
Authored by Chip Huyen, this book is, in my opinion, the Bible of Machine Learning Engineering. It covers every topic you need to know to get started in this field: Data Engineering fundamentals, Feature Engineering, Offline Evaluation, Model Compression, Quantisation, etc.
๐ธ Designing Data-Intensive Applications
Written by Martin Keppmann, this book offers an amazing introduction to data storage technologies and distributed systems. If you want to know the inner workings of NoSQL datastores, message brokers, etc. this is your book.
๐ธ AI Engineering (still reading it!)
The highly anticipated follow up to DMLS! I. In this book, Chip Huyen provides a structured perspective on the emerging field of AI Engineering, covering its domain, responsibilities and scope.
By the way, would you like a detailed post about AI Engineering? Let me know in the comments! ๐
๐ Content Creators
In Summer 2024, I started taking LinkedIn seriously - not just as another social network for endless scrolling, but as a powerful way to expand my knowledge on MLOps, LLMOps, ML, and AI in general.
Thatโs why I want to share with you some of the amazing profiles I follow, who consistently share high value content on my favorite topics. Here we go!
๐ธ Akshay Pachaar - Amazing content on GenAI, LLMs and Agents. Heโs also the co-founder of Daily Dose of Data Science!
๐ธ Alex Razvant - Expert insights on E2E ML Systems. His diagrams are on another level.
๐ธ Alex Vesa - Great insights about ML / AI Systems. No PoCs, just real-world applications.
๐ธ Cornellius Yudha Wijaya - AI Engineer that posts relevant content on GenAI applications. His last posts about RAG are top quality.
๐ธ Dipanjan Sarkar - Highly detailed explanations and diagrams showcasing GenAI applications and architectures.
๐ธ Eduardo Ordax - AI can be overwhelming, so if you need a laugh, Eduardo is the master of AI-related memes.
๐ธ Maria Vechtonova - Want to learn about MLOps? Then you definitely need to check out Mariaโs content - also the Marvelous MLOps organization! Their work is amazing.
๐ธ Paul Iusztin - High quality content about building production-grade ML / AI Systems. From RecSys, to RAG or LLM Twins!
๐ธ Paolo Perrone - Creator of the Tech Audience Accelerator. He also posts quality content on DS and ML.
๐ธ Pau Labarta Bajo - If you want to learn how to build real world ML systems, heโs your guy.
๐ธ Raphaรซl Hoogvliets - Great content on MLOps and MLE. Also raises very interesting topics and discussions.
This is my ML Engineer 2025 resource listโwhat do you think? Am I missing someone? Let me know in the comments!
Iโll see you next Wednesday but, in the meantime โฆ
Happy learning! ๐ง








![Designing Machine Learning Systems[Book] Designing Machine Learning Systems[Book]](https://substackcdn.com/image/fetch/$s_!ZF48!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F4dd7fab4-0b3f-4cde-8a0d-f2d2528e0f84_1200x630.jpeg)
![Designing Data-Intensive Applications[Book] Designing Data-Intensive Applications[Book]](https://substackcdn.com/image/fetch/$s_!b-1P!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fac1b29b9-8c70-43db-bfb6-1372654f620b_1200x630.jpeg)
![Cover - AI Engineering [Book] Cover - AI Engineering [Book]](https://substackcdn.com/image/fetch/$s_!HSvp!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F02180f27-95b5-4731-bbbd-848349a5b6ba_1200x630.png)
This is amazing, I looking forward to be like you, thank you for the road map๐๐.
Clear roadmap makes AI engineering career achievable.โ