Starting simple (Logistic Regression/LightGBM) before moving to complex models (Deep Learning/Transformers).
: Unlike resources focused solely on modeling, this guide addresses data collection, feature engineering, offline/online evaluation metrics, and scalable deployment. Pros and Cons Pros : Highly effective for FAANG-level interview preparation .
Ali Aminian is a prominent currently at Adobe , where he leads generative AI efforts for the Firefly team. His background includes developing large-scale ML systems at Google and lecturing at Stanford University on graduate-level ML topics. He co-authored this guide with Alex Xu, the creator of the popular ByteByteGo platform. Core Content: The 7-Step Framework Ali Aminian is a prominent currently at Adobe
To visualize how this framework operates in practice, consider this condensed blueprint for designing a video recommendation system (similar to YouTube or Netflix).
Choose a model that matches the complexity of the problem and the latency constraints of the system. Core Content: The 7-Step Framework To visualize how
Determine where training data originates (user logs, historical databases, third-party APIs).
Choose between Online Inference (dynamic, low latency, computes predictions on the fly using a model server like Triton or TF Serving) and Offline/Batch Inference (precomputes predictions and stores them in a cache for rapid retrieval). Choose between Online Inference (dynamic
Never jump straight into choosing a model. Spend the first 5 to 10 minutes defining the scope of the problem.