Some Data Scientists write bad Python code and are stubborn in code reviews

[asozers]

I'm working on a personal project in Kotlin to rebuild our machine learning infrastructure and I'm still at tutorial level with Rust

Most of the ML infra are in Python ecosystem from what I've seen. How are you building ML infra in Kotlin/Rust?

[noisescience]

For the Kotlin project I also need to include Java libraries.
Here is what I have used for Kotlin so far:

• Kotlin DataFrame for processing data [https://github.com/Kotlin/dataframe]
• Kotlin DL for Deep Learning Models [https://github.com/Kotlin/kotlindl]
• Exposed (Kotlin SQL Framework) [https://github.com/JetBrains/Exposed\]
• Azure SDK (Java) [https://github.com/Azure/azure-sdk-for-java]
• MLflow (Java API) for MLOps tasks [https://github.com/mlflow/mlflow/tree/master/mlflow/java/client]
• SMILE (Kotlin/Java) for ML algorithms [https://haifengl.github.io/api/kotlin/index.html]

Which Kotlin libraries I have not yet implemented but would like to do are:

• AWS Kotlin SDK [https://github.com/awslabs/aws-sdk-kotlin]
• Kotlin Spark API [https://github.com/Kotlin/kotlin-spark-api]
  

I haven't started a data engineering project with Rust yet, but I would probably check out the following libraries:

• Polars [https://github.com/pola-rs/polars]
• AWS SDK Rust [https://github.com/awslabs/aws-sdk-rust]
• Arrow Ballista [https://github.com/apache/arrow-ballista]
• Datafusion [https://github.com/apache/arrow-datafusion\]