This episode of "How AI Is Built" is all about data processing for AI. Abhishek Choudhary and Nicolay discuss Spark and alternatives to process data so it is AI-ready. Spark is a distributed system that allows for fast data processing by utilizing memory. It uses a dataframe representation "RDD" to simplify data processing. When should you use Spark to process your data for your AI Systems? → Use Spark when: Your data exceeds terabytes in volumeYou expect unpredictable data growthYour pipeline involves multiple complex operationsYou already have a Spark cluster (e.g., Databricks)Your team has strong Spark expertiseYou need distributed computing for performanceBudget allows for Spark infrastructure costs→ Consider alternatives when: Dealing with datasets under 1TBIn early stages of AI developmentBudget constraints limit infrastructure spendingSimpler tools like Pandas or DuckDB sufficeSpark isn't always necessary. Evaluate your specific needs and resources before committing to a Spark-based solution for AI data processing. In today’s episode of How AI Is Built, Abhishek and I discuss data processing: When to use Spark vs. alternatives for data processingKey components of Spark: RDDs, DataFrames, and SQLIntegrating AI into data pipelinesChallenges with LLM latency and consistencyData storage strategies for AI workloadsOrchestration tools for data pipelinesTips for making LLMs more reliable in productionAbhishek Choudhary: LinkedInGitHubX (Twitter)Nicolay Gerold: ⁠LinkedIn⁠⁠X (Twitter)