Define churn and business goals Building on this foundation, we need a concrete, actionable definition of customer churn before we design models or interventions. Customer churn refers to the loss of a banking relationship that materially reduces future revenue or engagement—this includes account closures, dormancy beyond a business-defined window, and

Why reliability matters for LLMs When an LLM request fails at 99th-percentile latency or returns a hallucinated answer, the impact goes beyond a single user annoyance — it damages trust, increases operational cost, and creates regulatory risk. How do you ensure an LLM-based feature doesn’t become the single point of

Why SQL for Data Engineering When you’re preparing for technical interviews, the sharpest tool in your kit is often a clear, fast query written in SQL that solves a real business problem. Recruiters and hiring managers for data engineering roles expect you to move beyond toy examples and demonstrate fluency

Overview and Objectives Building on this foundation, we frame why a production-ready evaluation system matters for modern LLM development: experiments without rigorous tracking are noisy, non-reproducible, and hard to scale. Native LLM Evaluation needs to live where your data and compute already are, so you can compare model outputs against

Map funnel steps and events Building on this foundation, start by treating the conversion funnel as a precise map of user intent: identify the discrete funnel steps you care about, then agree on the exact events that signal each step. Conversion funnel and funnel steps should appear immediately in your

Core SQL Concepts Building on this foundation, mastering a few fundamental relational concepts will get you through most SQL interview questions and make you a stronger data engineer. Start by treating the relational model as the contract between your data and queries: tables represent entities, columns represent attributes, and foreign

Agentic AI: Definition and Overview Agentic AI transforms language models from passive answer generators into goal-driven systems that take actions in the world. In practice, Agentic AI means composing an autonomous LLM with planning, state, and tool access so the model can pursue objectives over multiple steps rather than only

Definitions: Conversational vs Agentic AI Building on this foundation, we need to be precise about two often-confused categories: Conversational AI and Agentic AI. Conversational AI refers to systems optimized for dialogue—understanding intent, maintaining context, and producing responses that keep a human engaged. Agentic AI refers to systems that take autonomous,

PyData stack: tools and ecosystem overview When you assemble a modern Python-based data platform, you’re really choosing an ecosystem of interoperable tools rather than a single monolithic framework. The PyData stack centers on NumPy for numerical arrays and pandas for tabular data manipulation; these two libraries define most in-memory data