A detailed walkthrough of taking a demand forecasting model from exploratory analysis to automated, monitored production systems. Covers data pipelines, feature engineering, MLflow tracking, FastAPI serving, Prefect orchestration, and continuous retraining at scale.
The AI Engineer role explained: bridging research and production by turning notebooks into deployable systems. A deep dive into the workflow from experimentation to production APIs, with real examples from dynamic pricing, forecasting, and recommendation systems.
A deep dive into building production-grade ML inference systems that handle millions of predictions per day, from model serving patterns and GPU optimization to feature caching, autoscaling, and observability at scale.
A deep dive into how Amazon's order processing system scales globally, from event-driven sagas and idempotency to inventory, payments, and multi-region fault isolation.
A deep dive into Spotify's event-driven data platform, from global ingestion and exactly-once streaming to online/offline feature stores, vector search, and continuous experimentation that powers recommendations and playlists at massive scale.
A deep dive into the distributed architecture and system design behind Uber's real-time rider-driver matching system, from geospatial indexing and dispatch algorithms to backpressure, degradation, and global scaling.
The fundamental shift from writing code to designing systems: how to think about scalability, trade-offs, and long-term maintainability from day one.
Practical principles for writing code that scales with your team and survives the test of time. From naming conventions to architectural patterns.
Building production-ready full-stack applications with modern frameworks, AI integration patterns, and AWS deployment strategies.
A systematic approach to choosing the right ML model: understanding your constraints, data characteristics, and deployment requirements before writing a single line of code.
A guide to data structures and algorithms, from fundamental concepts to advanced techniques, with real-world applications.