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 … well, I guess it’s time to close our laptops and explore careers in other fields, right? Programming is done. Developers are obsolete. This is the end.

Just kidding 😆

Today’s 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 🤣) to my current role as a Senior ML Engineer. Am I also an AI Engineer? That’s a discussion for another time.

I’ve categorized these resources into four groups:

🔹 Full Courses 📺
🔹 Blogs 🖥️
🔹 Books 📖
🔹 Content Creators 📱

Ready? Let’s begin!! 👇

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💠 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 😂?

If you recognize this slide, you have my respect 🫡

Here’s my top 3 course recommendations for aspiring Machine Learning Engineers:

🔸 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐢𝐧 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 - 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.

🔸 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, that’s the key behind production-ready ML Systems!

🔸 The Full Stack

Teaching you how to build ML Systems with a hands on approach. Amazing content, amazing interviews and, if you’re interested in LLMs, you can’t miss the LLM Bootcamp!

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💠 Blogs

Now that you know my top course recommendations, let’s take a look at the blogs!

🔸 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, don’t miss Applied LLMs, which follows the same approach but with a focus on LLMs

🔸 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, … even how to prepare ML interviews! You should definitely check it out.

🔸 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.

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💠 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 haven’t finished the last one yet 😅) - here they are:

🔸 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.

🔸 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.

🔸 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! 👇

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💠 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.

That’s 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!

🔸 Akshay Pachaar - Amazing content on GenAI, LLMs and Agents. He’s also the co-founder of Daily Dose of Data Science!

🔸 Alex Razvant - Expert insights on E2E ML Systems. His diagrams are on another level.

🔸 Alex Vesa - Great insights about ML / AI Systems. No PoCs, just real-world applications.

🔸 Cornellius Yudha Wijaya - AI Engineer that posts relevant content on GenAI applications. His last posts about RAG are top quality.

🔸 Dipanjan Sarkar - Highly detailed explanations and diagrams showcasing GenAI applications and architectures.

🔸 Eduardo Ordax - AI can be overwhelming, so if you need a laugh, Eduardo is the master of AI-related memes.

🔸 Maria Vechtonova - Want to learn about MLOps? Then you definitely need to check out Maria’s content - also the Marvelous MLOps organization! Their work is amazing.

🔸 Paul Iusztin - High quality content about building production-grade ML / AI Systems. From RecSys, to RAG or LLM Twins!

🔸 Paolo Perrone - Creator of the Tech Audience Accelerator. He also posts quality content on DS and ML.

🔸 Pau Labarta Bajo - If you want to learn how to build real world ML systems, he’s your guy.

🔸 Raphaël Hoogvliets - Great content on MLOps and MLE. Also raises very interesting topics and discussions.

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This is my ML Engineer 2025 resource list—what do you think? Am I missing someone? Let me know in the comments!

I’ll see you next Wednesday but, in the meantime …

Happy learning! 🧠

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