Databases, Machine Learning, Sql, Python, Data Pipelines, Model Deployment, Analytics Tools, Statistics

Python, Java, Q2Q2I, dietary labels, HNSW, Q2I2I, BM25, multi-modal data, graph-based methods, real-time signals, taxonomy tagging, dense vectors, LSH, LLMs, dish attributes, sequence models, U2I, structured metadata, generative retrieval, multi-task deep ranking models, structured knowledge, embedding quality, knowledge-graph features, collaborative filtering

Machine Learning, Hadoop, Sql, Deep Learning, Tensorflow, Data Quality, Pytorch, Gcp, MLops, Spark, Data Governance, Predictive Analytics, Azure, Python, AWS, Generative AI, LLMOps, Scikit-learn, reinforcement learning, Model Deployment

S3, Nosql, Tensorflow, Pytorch, Flask, Cosmos DB, Python, BigQuery, Redshift, Sql, Azure ML, Git, Spark, Databricks, FastAPI, Airflow, GenAI, Hugging Face, LangGraph, Vertex AI, vector DBs, LangChain, LLMs, Scikit-learn, SageMaker, RAG, LlamaIndex, CI CD for ML

data engineering , Machine Learning, Hadoop, Apis, Sql, Javascript, MLops, Data Visualization, Kubernetes, Python, RShiny, Dash, Streamlit

Tensorflow, Pytorch, XGBoost, Python, Sql, AWS Bedrock, scikit-learn, AWS SageMaker, LightGBM, MLOps frameworks, LLM GenAI tools

Tensorflow, Pytorch, Docker, FastAPI, Python, computer vision models, TensorRT, vector databases, data pipeline tools, LLM application development

Machine Learning, Pyspark, Python, Sql, Training, Feature Engineering, Data Pipeline Concepts, Inference