Data Science, Neural Networks, Image Processing, Python, R, Machine learning frameworks and libraries, Deep learning architectures, Deployment pipelines, Cloud AI services, Generative AI models

Unix, Devops, Automation Frameworks, Python, GitHub Copilot, Data quality concepts, AI models

PostgreSQL, Redis, Pytorch, Flask, FastAPI, Rest Apis, Python, TensorRT-LLM, Hugging Face Transformers, SGLang, vLLM, WebSockets, Ray Serve, NVIDIA Triton, Server-Sent Events, KServe

prophet , Azure Databricks, Knn, Random Forest, Nosql, Seaborn, Numpy, Docker, Python, Azure DevOps, Logistic Regression, Sql, Git, AR, Pandas, Azure Data Factory, Arima, scikit-learn, MLflow, ARMA, Matrix Profile, Azure Cloud Ecosystem, pyarrow, plotly, Azure Data Explorer, Azure Machine Learning Service, Kusto, Hive Metastore, Time Series Forecasting Models

Tensorflow, Spark, Kafka, Machine Learning, MLops, Deep Learning, Pytorch, Kubernetes, Python, XGBoost, Docker, Nlp, scikit-learn, ONNX, Airflow, MLflow, AI model optimization, anomaly detection, time series forecasting, Cv, Recommendation systems, TensorFlow Serving

Tensorflow, Performance Tuning, Opencv, Rest Apis, Deep Learning, Ocr, Pytorch, Machine Learning Algorithms, Google Cloud, AWS, Python, Azure, Docker, Generative AI, LLMs, feature engineering, data preprocessing, Tracking, parallelization, multimodal models, RAG, YOLO, Segmentation, ML Ops, GPU optimization, TensorRT, ONNX, distributed low-latency data pipelines, RTSP streaming video pipelines, Classification, detection, multi-object detection

containerization , Python, Kubernetes, MLops, Security best practices, Monitoring and observability, Infrastructure as Code