Every stakeholder in the AI/ML field, from aspiring engineers to seasoned professionals, needs a structured learning path to navigate the different phases of development, from foundational theory to production-ready systems.
The educational landscape is already crowded with numerous options, many offering similar curricula. This abundance can make selecting the right resources a daunting task.
In this article, we’ll explore key factors for building a strong learning foundation and present a curated list of resources that can support your journey to becoming an ML/AI Engineer.
Choosing Resources for Your Learning Path
While it may be tempting to choose resources based on hype, it’s important to consider several key factors to ensure a comprehensive and practical education:
Foundational Strength
A resource’s impact can be measured by its ability to deliver core knowledge, establish best practices, and support the long-term sustainability of your skills. Resources that consistently meet these criteria are more likely to drive career success.
Practical Application
Effective learning resources often benefit from a focus on real-world application, connecting theoretical concepts to the engineering challenges faced in production environments.
Industry Relevance
An engaged community and instruction from leading industry experts ensure continuous improvement and offer a strong support network, which is essential for keeping skills relevant and useful over time.
Alignment with Emerging Trends
Resources that align with cutting-edge trends, such as MLOps, LLMOps, and production-grade system design, allow professionals to tap into the latest capabilities, ensuring their skill sets stay competitive and innovative.
By considering these factors, you can make informed decisions and select resources that truly support your professional goals.
1. Full Courses
🔸 Machine Learning in Production (Course — MLOps)
A foundational course for building ML production systems. It covers essential topics such as modeling strategies, pipeline monitoring, experiment tracking, and data augmentation, making it a must for MLE beginners.
🔸 Made with ML (Course — Software Engineering for ML)
This high-quality course shows how to combine Machine Learning with Software Engineering best practices. It emphasizes the principles required to build and maintain production-ready ML systems.
🔸 The Full Stack (Course — Hands-on System Design)
Teaching how to build ML systems with a hands-on approach. The course offers excellent content and interviews, with a dedicated LLM Bootcamp for those specializing in large language models.
2. Blogs
🔸 ApplyingML (Blog — Advanced System Design)
This blog provides highly detailed articles on system design, Feature Stores, ML testing, and counterfactual evaluation. It also offers a dedicated section, Applied LLMs, focusing on large language models.
🔸 Chip Huyen Blog (Blog — MLOps and Industry Insights)
This resource covers a wide range of relevant topics, including MLOps, ML systems, and LLM theory. It also provides valuable guidance on preparing for ML-focused interviews.
🔸 Eugene Yan Blog (Blog — Real-World Applications)
This blog is a key resource in the ML Engineering field. It covers technical topics such as LLM patterns, System Design for Recommender Systems, and advanced testing strategies for ML pipelines.
3. Books
🔸 Designing Machine Learning Systems (Book — Foundational Principles)
This book is a comprehensive guide to Machine Learning Engineering. It covers every critical topic for getting started, including Data Engineering fundamentals, Feature Engineering, offline evaluation, and model optimization techniques like compression and quantization.
🔸 Designing Data-Intensive Applications (Book — Data Infrastructure)
This book offers a critical introduction to data storage technologies and distributed systems. It is an essential resource for understanding the inner workings of NoSQL datastores, message brokers, and other core components of modern data infrastructure.
🔸 AI Engineering (Book — Emerging Methodologies)
This book provides a structured perspective on the emerging field of AI Engineering. It formally covers the domain, responsibilities, and scope of the role, defining the next generation of building AI systems.
4. Content Creators & Thought Leadership
To stay current, it is essential to follow industry leaders who consistently share high-value content on MLOps, LLMOps, and AI.
Engaging with their work on platforms like LinkedIn provides direct access to expert insights and discussions on real-world applications.
Key voices to follow include:
Akshay Pachaar, Alex Razvant, Alex Vesa, Cornellius Yudha Wijaya, Dipanjan Sarkar, Eduardo Ordax, Maria Vechtonova, Paul Iusztin, Paolo Perrone, Pau Labarta Bajo, Sabrina Romano, and Raphaël Hoogvliets.
When selecting learning resources, it’s crucial to evaluate them based on factors like foundational strength, practical application, and relevance to emerging trends. These considerations will guide you in choosing resources that not only fit your current needs but also position your career for future success.
Different resources address similar topics in unique ways. It is essential to align your choices with your specific career goals, whether that means focusing on MLOps fundamentals, advanced system design, or the emerging discipline of AI Engineering.
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