Over the past year, my professional focus has significantly broadened to encompass artificial intelligence and machine learning. While my initial forays into AI began with ChatGPT in early 2023, my first tangible AI-driven project materialized in September 2024. This involved participating in a challenge co-organized by Platzi and NewRelic, where, leveraging GitHub Copilot, I successfully debugged and enhanced an Angular application. This experience was particularly notable as my primary expertise lies in SRE/DevOps, demonstrating the practical application of AI in development tasks for an infrastructure-focused role.

A few months later, my exploration deepened into local AI tools such as Ollama, GPT4All, and LM Studio. My approach was largely hands-on, embracing a “learn by doing” philosophy that mirrored my early experiences with Docker. Much like when I first started with Docker, I began by installing software and following tutorials without a complete understanding, gradually building deeper knowledge through consistent usage and dedicated study.

December 2024 brought another pivotal moment with a challenge from CodigoFacilito and Microsoft. This intensive program not only expanded my AI/ML knowledge base but also culminated in achieving the Azure AI Engineer Associate certification. During this preparatory phase, I collaborated with Leonel Alberto on a two-week project. Beyond applying our AI/Python skills, the most profound takeaway was the critical importance of communication and organizational prowess in delivering a comprehensive product – not just the software, but also robust documentation and clear presentations. This truly highlighted the value of teamwork, and I strongly advocate for participating in such challenges for developing both technical and interpersonal skills.

Following the Azure AI Engineer certification, I was motivated to pursue the AWS Certified Machine Learning Engineer – Associate certification. Given my extensive background with AWS as my primary cloud provider, I successfully passed this exam in early 2025. My engagement with AWS initiatives continued through programs like AWS Build Games, where I designed and constructed a retro game from scratch using the Amazon Q CLI.

Feeling increasingly confident in my AI/ML understanding, I advanced my learning journey by reading Chip Huyen’s O’Reilly book, “Designing Machine Learning Systems.” I found it to be an exceptionally well-crafted and logically structured resource, rich with invaluable references, practical advice, and insightful personal observations. This book has become a foundational text that I will frequently consult.

Complementing my reading, I also immersed myself in the Machine Learning Specialty Course by Stephanee Maarek and Frank Kane on Udemy, as well as material provided by Antonio Feregrino via CodigoFacilito. Both instructors stand out for their excellent pedagogical skills and their ability to convey practical, real-world experience, making their content highly recommended.

The past year has been an incredibly transformative period of learning, feeling much longer than twelve months. Looking forward, my immediate goals are:

1. To develop another practical machine learning project.
2. To delve into “Effective Data Science Infrastructure” through dedicated study.
3. To successfully achieve the AWS Machine Learning Specialty Certification.

I am genuinely interested in hearing about your experiences. How has your personal learning journey and adaptation to the world of AI/ML unfolded? What recommendations do you have for me or others who are on a similar path? I look forward to reading your comments.