Question: Alternative to MLeap for Real-Time Inference Without Spark Context with SparkXGBClassifier

### Question: Alternative to MLeap for Real-Time Inference Without Spark Context

We are exploring alternatives to **[MLeap](https://github.com/combust/mleap)** for running inference without Spark, since MLeap has limitations with Spark/PySpark version compatibility and library updates.

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#### Our Setup & Goal
- **Environment**: PySpark 3.5.5
- **Algorithm**: Distributed ML training using XGBoost with Spark.
- **Goal**: Run **real-time inference** without requiring a Spark session/context, to reduce overhead and response latency.

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#### What We Did
1. Took a dataset (Titanic), converted it to Parquet, and split it into 80% (train) and 20% (test).
2. Trained with Spark (80% data) including preprocessing + XGBoost.
3. Evaluated on Spark (20% data) and logged the trained model.
4. Tried multiple logging/serialization approaches:
- MLflow `pyfunc`
- ONNX
- XGBoost native model
5. For inference: loaded the same 20% data, applied preprocessing **outside Spark**, reloaded the trained model, and ran predictions.

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#### The Problem
- In all approaches tested (MLflow `pyfunc`, ONNX, XGBoost native save/load), **accuracy differs** between:
- Spark-based evaluation (during training)
- Non-Spark inference (real-time service)
- It seems **precision is lost** when the model is saved and reloaded outside Spark.

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#### Main Requirement
- The accuracy from **Spark-based evaluation** and **non-Spark inference** must match.
- Need a solution to serialize/deserialize models that works across Spark training and non-Spark inference.
- Prefer portable formats (JSON or similar).
- Must avoid Spark context overhead at inference for real-time serving.

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#### Question
👉 Is there any solution or alternative to **MLeap** for serving models trained with Spark (e.g., XGBoost with PySpark), but performing inference outside of Spark (lightweight, real-time)?

- Should support **PySpark 3.5.5**
- Must work with **XGBoost distributed training**
- Should prevent accuracy mismatch between Spark and non-Spark inference
- JSON or portable serialization preferred

Any recommendations for frameworks, libraries, or best practices beyond MLeap would be greatly appreciated.