Geeky Codes | AI Engineering, Data Engineering & Machine Learning Tutorials
Learn what Apache Spark is, why it has become the standard for big data processing, how it works at a high level, and where it fits into today’s data engineering ecosystem. Introduction If you’ve started exploring data engineering, you’ve probably noticed one technology appearing everywhere—Apache Spark. Whether you’re working with Databricks, Delta Lake, Azure Data … Read more
Learn why ChatGPT, Claude, Gemini, and Llama sometimes generate incorrect information, what causes hallucinations, and how production AI engineers minimize them using RAG, grounding, guardrails, and evaluation. Find all tutorials here Table of Contents Introduction If you’ve used ChatGPT long enough, you’ve probably experienced something like this. You ask: Who won the FIFA World Cup in … Read more
Learn how RAG works, why LLMs hallucinate, and build your first Retrieval-Augmented Generation pipeline in Python. Find all tutorials here Introduction Large Language Models (LLMs) have transformed how we build AI applications. Today, we can ask models to: Tools like ChatGPT, Claude, Gemini, and Llama make these tasks feel almost magical. But there’s one major … Read more
After the fundamentals, RAG, LLM inference, and GenAI architecture questions, here are 20 fresh AI Engineer interview questions designed to test whether you can reason about real-world AI systems — not just explain frameworks. Part 1: AI Engineer Interview Questions Part 2: AI Engineer Interview QuestionsPart 3: AI Engineer Interview QuestionsPart 4: AI Engineer Interview Questions Part 5: AI … Read more
After interviewing 50+ AI Engineer candidates, I noticed a pattern: impressive GenAI projects can get you through the first 10 minutes—but strong fundamentals are what separate candidates who build AI systems from those who truly understand them. I’ve interviewed 50+ AI Engineer candidates over the past few months. Almost every resume looked impressive. RAG. AI … Read more
When your LLM returns malformed JSON, the problem isn’t always the model. Here’s how to build structured, validated, and production-ready outputs with Pydantic and Instructor. Your LLM application works perfectly in development. You deploy it. A few hours later, production logs start filling up with errors: Or perhaps: The frustrating part? The model’s answer looks … Read more
To prepare for TCS NQT effectively in just 10 days, focus on mastering key coding patterns rather than trying to learn everything. Prioritize essential topics like number manipulation, strings, and arrays. Utilize previous year questions for practice, simulate exam conditions, and revise weak areas. The goal is efficient preparation and confidence under pressure.
A practical guide to reducing cloud spend without sacrificing performance, reliability, or SLAs A Databricks ETL pipeline can be technically optimized and still be financially inefficient. You may reduce a Spark job from 90 minutes to 30 minutes—but if the optimized version uses a cluster that costs three times as much, your cloud bill may … Read more
This series emphasizes the importance of practical knowledge in Data Engineering interviews, focusing on scenario-based questions that assess candidates’ ability to design and manage data pipelines. Key topics include pipeline architecture, incremental loading, Change Data Capture (CDC), Delta Lake functionalities, and monitoring strategies, highlighting the need for a comprehensive understanding of data lifecycles.
Data Engineering interviews at mid-to-senior levels focus on complex real-world problems beyond basic SQL and ETL. Key topics include performance bottlenecks, data skew, and using SQL window functions like ROW_NUMBER(), RANK(), and DENSE_RANK(). Candidates must demonstrate their understanding of optimizing Spark jobs and partitioning strategies in data management.
A practical framework for debugging hallucinations in production RAG systems—from retrieval and chunking to prompts, generation, evaluation, and governance Part 1: AI Engineer Interview Questions Part 2: AI Engineer Interview QuestionsPart 3: AI Engineer Interview QuestionsPart 4: AI Engineer Interview Questions Part 5: AI Engineer Interview Questions One of the most interesting questions I have seen in … Read more
A practical framework for turning ambiguous business problems into production-ready ML solutions Part 1: AI Engineer Interview Questions Part 2: AI Engineer Interview QuestionsPart 3: AI Engineer Interview QuestionsPart 4: AI Engineer Interview Questions Part 5: AI Engineer Interview Questions I recently came across an AI Engineer interview question that sounded deceptively simple: “Your movie is releasing … Read more
In Part 1, we covered the fundamentals of Spark, PySpark, Databricks, DAGs, lazy evaluation, partitioning, data skew, salting, AQE, migration validation, and common PySpark coding questions. But experienced Data Engineer interviews usually go one level deeper. Interviewers want to know: Can you actually design, optimize, and troubleshoot a production data platform? In this article, we’ll cover … Read more
RAG, LLMs, Agentic AI, LangGraph, and Production GenAI Part 1: AI Engineer Interview Questions Part 2: AI Engineer Interview QuestionsPart 3: AI Engineer Interview QuestionsPart 4: AI Engineer Interview Questions Part 5: AI Engineer Interview Questions AI Engineer interviews are no longer limited to questions like: “What is an embedding?” or: “What is RAG?” Interviewers increasingly want … Read more
The article discusses the Pandas SettingWithCopyWarning, which arises when modifying a DataFrame’s slice, creating ambiguity about whether the object is a view or a copy. It advocates for clear coding practices by using .loc[] to alter the original DataFrame and .copy() to create independent subsets, ensuring predictable behavior and maintaining data integrity.
Stop scanning the entire S3 dataset when you only need a handful of Parquet files When working with large datasets on AWS S3, Parquet is one of the most popular storage formats for data engineering and analytics workloads. It’s columnar. It’s compressed. It supports predicate pushdown. And it works extremely well with distributed processing frameworks … Read more
Before self-attention changed AI forever, recurrent neural networks tried to solve sequence modeling. Here’s why they eventually hit a wall. This is Part 1 of a 5-part series on Transformers. 📚 Blog Series Every Revolution Starts With a Problem In 2017, Google published a paper that fundamentally changed artificial intelligence. Attention Is All You Need. … Read more
Data Engineering interviews now demand in-depth knowledge of Spark, PySpark, and Databricks beyond basic SQL and transformations. Candidates should understand concepts like Spark architecture, lazy evaluation, DAG, transformations, data skew, and troubleshooting techniques. Strong candidates demonstrate proficiency across coding, architecture, and production troubleshooting, distinguishing them in interviews.
Introduction Imagine you’ve built an amazing house price prediction website(flask ML App) using Flask and a machine learning model. The application works perfectly. Users enter details like location, area, number of bedrooms, and the model predicts the property’s price within milliseconds. There’s just one problem. Most of the day, nobody is using it. Yet your AWS … Read more
Understanding Nested Parallelism,FastAPI, OpenMP Thread Contention, and the Right Way to Deploy CPU-Bound Machine Learning Models Imagine This… You have trained a Random Forest classifier using Scikit-Learn. Everything works perfectly during development. You deploy it behind FastAPI, add a bit of asynchronous programming using run_in_executor(), and your API happily serves requests. Then someone runs a … Read more