Alpha Omondi Ogilo

Cons 1. Lack of Depth: The course focuses on "pure code typing" rather than teaching engineering. The instructor narrates exact his typing every single line, but rarely explains the underlying "why" or "how." at both high and granular level. He seems knowledgeable but lacks teaching skill. It feels like a copy-pasted production project with shallow explanations. A major example of this oversight is the missing evaluate_fraud_model_performance() Celery task in ./backend/app/core/task/ml.py, which is absent from both the course and the GitHub repository. 2. Weak AI/ML Sections: (You will curse and give up this entire section if you don't have extensive prior AI/ML knowledge and experience.) The instructor simply reads from two summary markdown files as if that will help us any true understanding. I spent double the course time doing outside research just to understand what over 3,000 lines of code in this section are doing. 3. For those on Windows + Docker Desktop has to run the containers on WSL as volume mount does NOT work on standard windows file system. In addition, you will have to toggle configurations between WSL and localhost so that Traefik can auto detect other containerized services. Good luck on this lol. 4. Distracting Production: The constant screen zooming, highlighting, and magnification—combined with low-volume mumbling and distracting background music—is incredibly taxing. It causes significant visual and auditory strain, making it difficult to maintain focus. 5. Ineffective Delivery: The instructor prioritizes visual effects over clear communication. (Please study top-tier instructors like Maximilian Schwarzmüller, Ed Donner, or Stephen Grider to see how complex technical content should be delivered.) 6. Repetitive Verbal Tics: I lost track of how many hundreds of times he said "it’s going to be," "like that," and "just like that" as he types every single line. These verbal crutches are extremely bothersome and make the learning experience mentally exhausting. Pros 1. Breadth of Concept: Be prepared to spend at least 100 additional hours self-studying to truly grasp the implementation. This course is a solid choice if you want exposure to full modern fintech stack. 2. Unmatched (but Unrealistic) Scope: The sheer volume of knowledge condensed into this single course is mind-boggling. It covers several distinct specializations: -Distributed Systems: Celery, RabbitMQ, Redis, worker management, rate limiting, and beat scheduling. This alone should be 30 hour course! -Backend Engineering: FastAPI, SQLModel, async PostgreSQL, and complex financial transaction flows. This alone should be 30 hour course! -ML Engineering: Feature engineering, gradient boosting, pipelines, and data leakage prevention. This alone should be 30 hour course! -MLOps: MLflow, model registries, drift detection, and retraining pipelines—a cutting-edge field in itself. A relatively new field that barely existed 5 years ago! -Production Deployment: Yet another full specialization. This alone should be 10 hour course!
- Review by Juan Parque on FastAPI & Machine Learning: Build a Banking Fraud Detection