Data Processing For AI, Integrating AI Into Data Pipelines, Spark

Data Processing for AI, Integrating AI into Data Pipelines, Spark | ep 16

3d ago 46:26

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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 volume
You expect unpredictable data growth
Your pipeline involves multiple complex operations
You already have a Spark cluster (e.g., Databricks)
Your team has strong Spark expertise
You need distributed computing for performance
Budget allows for Spark infrastructure costs

→ Consider alternatives when:

Dealing with datasets under 1TB
In early stages of AI development
Budget constraints limit infrastructure spending
Simpler tools like Pandas or DuckDB suffice

Spark 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 processing
Key components of Spark: RDDs, DataFrames, and SQL
Integrating AI into data pipelines
Challenges with LLM latency and consistency
Data storage strategies for AI workloads
Orchestration tools for data pipelines
Tips for making LLMs more reliable in production

Abhishek Choudhary:

Nicolay Gerold:

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