When building ML systems, most people focus on the model.
But in production, the hard part is not training —
it’s data, deployment, versioning, and serving.
Modern ML engineering solves this using the FTI pattern:
Feature → Training → Inference
This is like:
DB → Backend → UI
🔹 Why we need ML pipelines
A real ML system must handle:
data ingestion
feature computation
model training
model versioning
deployment
monitoring
rollback
scaling
Without structure → chaos.
🔹 1. Feature Pipeline
raw data → features → feature store
Responsibilities:
collect data
clean & validate
compute features
compute labels
version data
Features are saved in a feature store.
Why?
To avoid training / inference mismatch.
This solves:
training-serving skew
🔹 2. Training Pipeline
features → training → model → model registry
Responsibilities:
load features
train model
evaluate
version model
store metadata
Models are saved in a model registry.
So we always know:
model v1 → features F1 F2 F3
model v2 → features F2 F3 F4
This makes rollback easy.
🔹 3. Inference Pipeline
features + model → prediction
Inputs:
feature store
model registry
Outputs:
predictions
text
scores
embeddings
Can be:
batch
real-time API
streaming
Everything is versioned → safe deployment.
🔹 Why FTI is powerful
Instead of 20 components:
Feature
Training
Inference
Each pipeline can:
run separately
scale separately
use different tech
be built by different teams
Perfect for production ML.
🔹 Works great for LLM / RAG / AI apps
Example for LLM Twin:
Feature
→ collect posts
→ create embeddings
Training
→ fine-tune model
Inference
→ retrieve context
→ generate text
Same pattern.
Different data.
✅ Rule to remember
Every real ML system = Feature + Training + Inference
Understand this →
you can design almost any ML architecture.
Top comments (3)
interesting
Great
That's very interesting.