This project is a comprehensive machine learning interview guide and technical study resource designed for individuals preparing for machine learning and AI engineering roles. It provides a collection of materials and practice problems covering core algorithms, theoretical fundamentals, and the implementation of neural network architectures.
The resource serves as a technical reference for generative AI development, focusing on the design and optimization of large language models and diffusion systems. It includes frameworks for system design, covering the architecture of production machine learning pipelines, retrieval pipelines, agentic workflows, and the reduction of latency and memory footprints through inference optimization.
Beyond model architecture, the project covers MLOps deployment workflows, including A/B testing and canary releases, as well as model evaluation and validation strategies. It also provides coaching for behavioral interviews, utilizing structured communication frameworks to handle professional and situational questions.
The project is implemented as a collection of Jupyter Notebooks.
