MLops, Databricks, Python, Pandas, XGBoost, Spark, LIME, LightGBM, MLflow, scikit-learn, SHAP

Pytorch, Tensorflow, Python, XGBoost, Machine Learning, Data Pipelines, Data Processing, Experimentation Methodologies, scikit-learn

Pyspark, Tensorflow, Numpy, Git, Pandas, Pytorch, XGBoost, Keras, Python, scikit-learn, LightGBM, TensorFlow Serving, TensorRT, MLflow, ONNX, Azure ML Pipelines, Kubeflow, TorchServe, CatBoost

C, Lvm, Design Patterns, Windows, Cuda, Pytorch, Linux, Os Concepts, Opencl, Python, MLC, Fixed-point representations, Quantization concepts, llama.cpp, Llm, TFLite, ONNX Runtime, Optimizing algorithms for AI hardware accelerators, MLX, Generative AI models, Kernel development for SIMD architectures, SIMD processor architecture