Expanding My Cloud Journey from Solutions Architect to Machine Learning
Two years ago, I started learning about the world of cloud computing by passing the AWS Certified Solutions Architect—Associate Certification exam. It was more about laying the groundwork for my learning journey and pushing me to think critically about how to design efficient systems that solve real-world problems.
Learning about AWS tools and their use cases has a direct impact on my work. Many examples directly mirrored the challenges I face in my current role. It inspires me to create more robust and creative tools, and make better design decisions, whether it is optimizing data processing pipelines, creating more intuitive user interfaces, or designing more efficient software architectures.
Recently, I decided to revisit with a focus on machine learning. Taking the AWS Machine Learning Specialty exam was more than just earning another credential. While I had some prior experience with machine learning projects, I felt the need to revisit and refine my skills — to understand not just the theory but also the practical applications of machine learning in the cloud. Furthermore, it was something that I would like to continue exploring and brushing up on my skills.
Preparing for the AWS Machine Learning Specialty Exam was exciting and challenging. I started with the basics, revisiting key concepts like supervised and unsupervised learning, feature engineering, and evaluation metrics (e.g., model bias and variance, hyperparameter tuning, cross-validation, dimensionality reduction, etc.) I relied on a mix of online courses, in-depth AWS documentation, and practice exams to enhance my learning and helped me grasp key aspects like data preprocessing, appropriate algorithm selection, and scaling ML models effectively.
Juggling my full-time workload, a foreign language class, and exam preparation was not easy. However, by maintaining a consistent study schedule and utilizing Google Calendar to plan, I was able to keep on track. So, what’s next? I’m hoping to put these ML skills to good use at work and potentially investigate their application in quantum and personal projects.
