A New Path — Master’s in Machine Learning Engineering
Well guys, I proudly would like to share that in this month I’ll finally begin my Master’s in ML Engineering.
Well, I’ve been extremely busy. Last year, I was in Italy obtaining my Italian citizenship. Then, I returned to Brazil in November 2024 for surgery on my back, and at the beginning of December 2024, I signed up for a Master’s in ML Engineering.
I chose FIAP (Faculdade de Informática e Administração Paulista), probably the best tech university in Brazil.
Why FIAP?
Almost everything I have learned so far, I have learned on an online tech platform called “Alura”. Alura and FIAP are now partners, and I received a discount. However, price was not the most significant factor in my decision. I was looking for something I couldn’t quite describe, so I conducted in-depth research on all “data something…” courses — Data Engineering, Data Analytics, Data Science — and after this very long research, I found that FIAP has the best curriculum.
Considering the price, the discount, and the curriculum itself, FIAP stood out as my choice, surpassing all other universities in Brazil.
Luckily, they offer many tech courses at both MBA and Master’s levels (called Pos-Tech). The MBA itself is still beyond my financial means, so the more affordable “Pos-Tech” was my choice.
But I hadn’t chosen the course yet, and there were two options:
- Data Analytics
- Machine Learning Engineering
After comparing both, I realized that the Data Analytics course wouldn’t teach me much more than I already knew. However, the ML Engineering course was a perfect fit for me, aligning with my goals and bridging some knowledge gaps I currently have.
Machine Learning Engineering — Course Phases
Phase 1: ML Fundamentals
Introduction to AI and Machine Learning concepts, with hands-on experience in Python and cloud-based ML solutions.
Phase 2: Big Data Architecture
Exploring data storage structures, Big Data platforms, and relational and non-relational databases.
Phase 3: Supervised and Unsupervised Learning
Training classification, regression models, and techniques like clustering and dimensionality reduction.
Phase 4: Deep Learning and AI
Focus on neural networks, NLP, computer vision, and generative AI, including GANs and LLMs.
Phase 5: MLOps
Focus on containers, construction of feature store, versioning, CI/CD, compliance and management of ML models.
Conclusion
At the end of each phase they have a so-called “Tech Challenge” and by the end of the course we have a hackthon (or datathon).
Summing it up
Until now, I’m positively impressed by FIAP. Everything has been well-communicated and organized (the university itself uses Discord for communication… I hate WhatsApp). I also love the idea of Tech Challenges and Datathons, which are said to be based on real cases from partner companies.
They also have a festival called FIAP Next for tech startups.
Extra:
You can follow my learning through this Master’s Course here: github repo.
If you want to know how everything started, I suggest you read this post.
Igor Comune.
