Connecting the dots in data and AI systems
Simplifying MLE & MLOps with the FTI Architecture
Data and AI systems are a mess.
They are complex and hard to grasp.
If you just started working in AI or have been working for a few years, it’s hard to see how the worlds of data engineering, research (DS, ML), and production (AIE, MLE, MLOps) come together into a single homogenous system.
As a data engineer, you finish your work by ingesting the standardized data into a data warehouse or lake.
As a researcher, your work finishes when you train the best model on a static dataset and push it to a model registry.
As an AIE or MLE, your work finishes when serving the model to production.
As an MLOps engineer, your work finishes when the operations are automated and monitored adequately for long-term robustness.
But is there a more accessible and intuitive way to understand the entire end-to-end data and AI system?
Yes! Through the FTI architecture.
Let’s quickly dig into the FTI architecture and apply it to a production LLM & RAG use case.
