Apache Spark, Sql, Tensorflow, Pytorch, Databricks, Python, Machine Learning Algorithms, model versioning, deep learning frameworks, Lakehouse architectures, cloud platforms, MLflow, Monitoring, MLOps practices, Statistical Modeling, drift detection, CI CD for ML, automated retraining

Matplotlib, Hadoop, Power Bi, data mining, Big Data Technologies, Tableau, Google Cloud, Spark, Azure, Python, AWS, Machine Learning Algorithms, data visualization tools, R, MLOps practices, Statistical Modeling, cloud-based data platforms, feature engineering

Java, Scala, Sql, Nosql, Gcp, Data Architecture, Spark, Databricks, Azure, Kubernetes, Python, AWS, Event-driven systems, Delta Lake

Machine Learning, Natural Language Processing, MLops, Artificial Intelligence, Deep Learning, cloud, XGBoost, Tensorflow, Pytorch, generative ai, model training, fine tuning, prompt engineering, peft, machine learning evaluation, embedding, ai agents, rag, large language models, vector database, lightgbm, ml systems

S3, PostgreSQL, Tensorflow, Lambda, Javascript, Ec2, Pytorch, Docker, XGBoost, Python, Node.js, Redis, Opencv, FastAPI, MongoDB, Kubernetes, scikit-learn, Hugging Face, TensorRT, TorchScript, Pinecone, ONNX, Stable Diffusion, Detectron2, controlnet, YOLO, AWS SageMaker, Diffusers

Computer Vision, Deep Learning, LangChain, VectorDBs, GenAI models optimization, Model deployment, RAG, Performance Tracking, Data ingestion and preparation, Feature engineering

Machine Learning, Python, Transformers, GenAI Technologies, AI Agents, Prompt Engineering, MLOps Practices

Spark, Azure, Python, AWS, Airflow, cloud ML platforms, MLFlow

Algorithms, Debugging, Data structures, Python, ML infrastructure, Model deployment, reinforcement learning, Data Processing, Optimization, Model evaluation