Python or R, MLOps practices and tools

Nltk, Natural Language Processing, Apache Spark, Tensorflow, Pandas, Pytorch, Gcp, Databricks, Azure, AWS, Langchain, scikit-learn, HuggingFace, vector databases, mlflow, gensim, OpenAI

Jax, Data structures, Tensorflow, Algorithms, Pytorch, System Design, Python, Scaling laws and evaluation methodologies, Distributed systems and ML infrastructure design, Transformers and attention mechanisms, Large-scale ML data pipelines, Tokenization and sampling strategies

Tensorflow, XGBoost, Numpy, Nlp, Pandas, Pytorch, Gcp, Python, LangChain, Vision AI, Scikit-learn, Gen AI, Document extraction, Conversational AI

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

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

Sql, MLops, Terraform, Arm, Azure Machine Learning, Python, ServiceNow AI ML Predictive Intelligence, Microsoft Azure AI, Infrastructure as Code, Virtual Agent, Azure OpenAI, API-based integrations, Kore.ai XO Platform, CI CD pipelines, Bicep

C, Tensorflow, Dsp, Pytorch, Python, compression techniques, ONNX, POSIX PTHREADS, RNNs, Audio Processing, Microcontroller architectures, ML frameworks, CNNs, fixed-point coding, model quantization, embedded solutions, audio framework, U-Nets, multi-threaded programming, Transformers