---
title: "Next steps"
description: "Continue to Part 3 for model serving and explore advanced topics."
---

# Next steps

> For the complete documentation index see: [llms.txt](https://aerospike.com/docs/llms.txt)
> 
> All documentation pages available in markdown.

## What’s next: Part 3

You now have the two artifacts that matter for deployment: a saved model and a reproducible training dataset definition. Part 3 shows how to use them in a request-time prediction path.

You will switch from Spark batch reads to low-latency Aerospike key lookups, use `get_feature_vector()`, and connect feature retrieval directly to model inference. This is where production constraints become real: even when model inference is fast, overall prediction latency depends on fetching the right features quickly for each request.

Continue to [Part 3: Model Serving](https://aerospike.com/docs/develop/model-serving/) and start at **Cell 14** in the same notebook.

## Project directory snapshot

If you followed the full tutorial, your project directory may look like this:

-   Directoryfeature-store-tutorial/
    
    -   aerospike-spark-5.0.1-spark3.5-scala2.13-clientunshaded.jar Spark connector
    -   feature\_store\_tutorial.ipynb Your Jupyter notebook (Parts 1 and 2)
    -   Directorymodels/
        
        -   Directorytrip\_decline\_risk\_lr/ Saved Logistic Regression model
            
            -   …
            
        
    -   Directorydatasets/
        
        -   Directorytrip-decline-risk-v1/ Materialized dataset Parquet files
            
            -   …
            
        
    

## Advanced topics

Once you’ve completed the tutorial, consider coming back and exploring other classifiers besides logistic regression. Try `RandomForestClassifier`, `GBTClassifier`, or `MultilayerPerceptronClassifier` from `pyspark.ml.classification` and evaluate how it performs on the same data.

## Related documentation

-   [Aerospike Spark connector](https://aerospike.com/docs/connectors/spark/) - Configuration options and advanced features
-   [Spark MLlib Guide](https://spark.apache.org/docs/latest/ml-guide.html) - Classification, regression, and clustering algorithms

::: undefined
-   I have completed the model training tutorial.
-   I know what to learn next.
:::

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::: undefined
-   Continue to Part 3: Model Serving to serve real-time predictions using Aerospike’s sub-millisecond reads.
    
-   Explore the [Aerospike Spark connector documentation](https://aerospike.com/docs/connectors/spark/) for advanced features.
:::

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Putting it all together](https://aerospike.com/docs/develop/model-training/step/4/part/0/putting-it-together)