GitHub user tanishqgandhi1908 closed a discussion: Proposal - Supporting
user-provided ML models in workflows
## Goal
Make Texera a platform where a user can **bring their own trained model and run
it inside a workflow** — upload the model, pick it in an operator, and get
predictions on the data stream.
## Context
- We already have a **Hugging Face operator** for hosted/third-party models —
that's a separate, ongoing effort.
- This proposal is about **models the user uploads themselves** (their own
trained weights), stored and versioned in Texera like datasets.
## Scope
- **Step 1: PyTorch** — first framework we support end-to-end.
- **Next:** TensorFlow, then scikit-learn — same flow, more loaders.
```mermaid
flowchart TD
Texera(["Texera"])
subgraph Packaging
HF[Hugging Face]
MLflow[MLflow]
ONNX[ONNX]
end
subgraph Libraries
TF[TensorFlow]
PT[PyTorch]
SK[scikit-learn]
end
subgraph Models["Model categories"]
LLM[LLM]
REG[Regression]
CLS[Classification]
TREE[Tree]
end
Texera --> HF
Texera --> MLflow
Texera --> ONNX
HF --> TF
HF --> PT
MLflow --> TF
MLflow --> PT
MLflow --> SK
ONNX --> PT
ONNX --> SK
TF --> LLM
TF --> REG
TF --> CLS
PT --> LLM
PT --> REG
PT --> CLS
SK --> REG
SK --> CLS
SK --> TREE
```
## Storage
Models are stored **exactly like datasets** — versioned in LakeFS with the
bytes in MinIO — so we reuse the existing upload, versioning, and
access-control stack. A model is simply an asset tagged with a type (`DATASET`
or `MODEL`); To namespace by type, logical file paths now carry a resource-type
prefix — `/datasets/<owner>/<name>/<version>/<file>` and, for models,
`/models/<owner>/<name>/<version>/<file>` — resolved through the same path →
LakeFS → MinIO chain. A dedicated **Models** section in the UI mirrors Datasets
for browsing and uploading.
<img width="1440" height="900" alt="image"
src="https://github.com/user-attachments/assets/59b713e2-c7b7-489b-9faa-5421ef06b72b";
/>
## Design (from #4198)
## Two user experiences
Both reuse the existing dataset storage (LakeFS/MinIO); a model is just an
uploaded asset the operator/UDF fetches at run time.
1. **No-code operator** — drag a "Model Inference" operator, select the
uploaded model, choose the input columns and the output column. No code. Best
for non-technical users.
<img width="1440" height="900" alt="image"
src="https://github.com/user-attachments/assets/13523ec7-7cef-4a5d-8348-00ebd989a6cb";
/>
2. **Python UDF** — select the model in the UDF's property panel; the framework
fetches + loads it and exposes it to your code, so you only write the inference
logic. For users who need custom pre/post-processing.
<img width="1440" height="900" alt="image"
src="https://github.com/user-attachments/assets/1bfe85ad-fc31-43a1-a9a6-9761285d8081";
/>
## Feedback wanted
We'd love feedback on the overall idea and the proposed architecture:
- **Library order:** does PyTorch → TensorFlow → scikit-learn match what users
need most?
- **Two UX:** any concerns with offering both the no-code operator and the UDF
(vs. just one)?
All thoughts, concerns, and alternative approaches welcome.
GitHub link: https://github.com/apache/texera/discussions/6041
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