MHP – A Porsche Company

- Developing multimodal Retrieval Augmented Generation applications on company data with Large Language Models
- Building an LLM-based Quality Evaluator and ML-based Duplicate Detection Method for Software Bug Tickets

Data Scientist

Bosch Power Tools

- Implementation and evaluation of probabilistic deep learning models for demand forecasting on AWS SageMaker

Master Student Data Science

- Development of a tool for automated data analysis and visualization in SAP Analytics Cloud
- Implementation of time series forecasting and outlier detection models

Working Student Data Science

Mercedes-Benz AG

- Speeding up the assembly process by using machine learning to predict throughput times and errors on Azure Databricks
- Automation of graph visualization for the optimization of assembly analysis

Fraunhofer IPA

- Implementation and evaluation of a generative deep learning model for image data augmentation for cutting edge patterns
- Implementation of scientific papers in Python

Bachelor Student Deep Learning

MAHLE

- Analysis of engine data and application of machine learning for prediction in piston temperature measurement

Intern Data Analysis & IT Project Management

I make learning Data Science effortless for you | Data Science @ Bosch

Hi 👋🏻
I'm Banias, Data Scientist at MHP.

Applying new technologies and methods in Data Science is my passion.

Through my experience and continual learning, I've gained valuable insights and tips that I love sharing with Data Scientists, Machine Learning Engineers and builders like you.

If you're looking for practical advice, helpful resources, and articles on the latest trends or best practices in the field, you're in the right place.

Let's connect and stay updated on the exciting world of Data Science together. Feel free to reach out, read my articles, and let's grow our knowledge and network. :)


Note: My views are my own and don‘t represent my employer.

Are you still calculating feature importance?

Use Permutation Importance too.

Permutation Importance calculates feature importance by randomly shuffling the values of a feature and observing how the model’s performance changes.

In comparison to Feature Importance, Permutation Importance works for every model (and not only for tree-based models).

With eli5, you can calculate Permutation Importance with ease in Python.

show_weights() will show you the features which hurts the performance the most, so they are more important.

Link to repo: github(dot)com/eli5-org/eli5

Banias Baabe

I teach AI Engineers the concepts and tools they need to stay ahead | AI Engineer @ Netze BW

I stumbled upon a fast alternative to NumPy.

With 𝗡𝘂𝗺𝗘𝘅𝗽𝗿 I transformed a sluggish 650 ms loop into a 60 ms calculation and that was on a single thread!

Here’s how NumExpr turbocharges your array computations:

• 𝗖𝗵𝘂𝗻𝗸𝗲𝗱, 𝗶𝗻-𝗰𝗮𝗰𝗵𝗲 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻: Avoids building giant temporaries by splitting arrays into cache-sized blocks and streaming them through a lightweight virtual machine.
• 𝗦𝗜𝗠𝗗 & 𝗩𝗠𝗟 𝗮𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗶𝗼𝗻: Leverages single-instruction-multiple-data instructions and-when available-Intel’s Math Kernel Library for transcendent functions.
• 𝗧𝗿𝘂𝗲 𝗺𝘂𝗹𝘁𝗶-𝗰𝗼𝗿𝗲 𝘀𝗰𝗮𝗹𝗶𝗻𝗴: Automatically farms out chunks to all your CPU cores, delivering up to 5×–15× speed-ups on complex expressions.

🔗 Link to repo: github(dot)com/pydata/numexpr


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