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

Hi 👋🏻
I'm Banias, AI Engineer at Netze BW.

Applying new technologies and methods in Data Science & AI 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.

Banias Baabe

Most people running local AI models have no idea which ones will actually work on their hardware.

𝗹𝗹𝗺𝗳𝗶𝘁 is a small open source CLI tool that scans your system and tells you exactly which models fit your setup.

No more downloading files that turn out to be too large to run.

Here is what it does:
✅ Evaluates 150+ models from 30 providers against your CPU, GPU, and RAM specs.
✅ Returns a ranked list of models that match your hardware.
✅ Connects with Ollama and LM Studio to detect and download models directly.
✅ Supports multi GPU setups and Mixture of Experts architectures.

It's a simple quality of life tool that saves real time for developers working with local AI.

🔗 Link to repo: github(.)com/AlexsJones/llmfit


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

Exponent

Senior Engineer

Senior Associate

May is a mechanical engineer in Exponent’s Thermal Science Practice with a background in combustion, heat transfer, thermodynamics, and fluid dynamics.  She specializes in multidimensional modeling and computational analysis of complex systems including industrial equipment, combustion system and fluid & heat transfer equipment with focus on failure analysis and fire and explosion investigations.

Associate, Thermal Science

Manager

Purdue University

Research Assistant

Cummins Inc.

Combustion/Performance/Emissions Engineer Intern

Cummins, Inc

Heavy Duty Product Planning Intern

Cummins Power Generation

G-Drive Controls Intern

Cummins Engine Company

Design Engineering Intern

May is a mechanical engineer in Exponent's Thermal Science Practice with a background in combustion, heat transfer, thermodynamics, and fluid dynamics. She specializes in multidimensional modeling and computational analysis of complex systems including consumer electronics, biomedical devices, industrial equipment, combustion systems and fluid & heat transfer equipment with focus on failure analysis and fire and explosion investigations. 

May has experience on consequence analysis associated with flammable releases and vapor cloud explosions in Oil & Gas facilities. She routinely performs facility evaluations for permitting and planning purposes. She specializes in computation modeling, using computational tools including FLACS, StarCCM+, CFX, and ANSYS Fluent. 

May has extensive experience in turbulent diffusion flames, soot formation for direct injection engine applications and performance and emission evaluations of diesel engines in test cells. She is proficient at coding in Fortran, C, Python, and MATLAB as well as parallelizing code with MPI (Message Passing Interface) and OpenMP. May regularly utilizes CAD software (Solidworks, Catia, ProE, Spaceclaim). 

She has extensive experience in modeling and testing of wearables and consumer electronics for performance and risk assessment of burn injury hazards by radiation, convection, and conduction. She is also experienced in the analysis and investigation of burn injuries by flame, scald, extreme environment, and electronics as well as frostbite.

May has performed engineering analysis using computational fluid dynamics (CFD) for blood flow, blood trauma, and fluid path analysis in medical devices such as heart pumps, specialized catheters, IV infusion devices, and blood oxygenators.

May applies her engineering expertise to intellectual property disputes. She has worked on several aspects of intellectual property litigation such as validity/invalidity, infringement/non-infringement, Markman hearings, and trade secrets. 

May performed her PhD research at Purdue University where she conducted multidimensional modeling of turbulent diffusion flames for diesel engine applications. She assessed the effect of exhaust gas recirculation, combustion chamber temperature, and injection pressure on fuel-air mixing and soot formation under direct injection engine conditions. May’s work focused on evaluating and developing soot models that were experimentally validated across several regimes and fuels types. Additionally, she has experience in evaluating performance and emissions of diesel engines in test cells.

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Banias Baabe

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The Battle

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Engagements

Likes

Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Banias Baabe's LinkedIn Performance

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