Machine learning techniques

Pyspark, Tensorflow, Pandas, MLops, Pytorch, Azure Data Lake, Spark, Databricks, Python, embeddings, scikit-learn, Hugging Face Transformers, Azure OpenAI Service, model versioning, MLflow, CI CD, Azure AI Foundry, prompt engineering, Unity Catalog, agentic workflows, RAG, drift detection, model monitoring

Java, Tensorflow, Machine Learning, Pytorch, Hadoop, Spark, Keras, Python

Data Science, Machine Learning, Data Analysis, Data Processing

Docker, FastAPI, Kubernetes, Python, Sql, AWS, Airflow, Dataiku, CPLEX, OR-Tools, Pulp

prophet , Machine Learning, Scipy, Random Forest, Deep Learning, Tensorflow, Numpy, Pandas, MLops, Pytorch, XGBoost, Arima, Databricks, Python, LSTM, scikit-learn, SARIMA, GRU, Time Series Forecasting, TimeGPT, statsmodels

data engineering , Large Language Models (LLMs), Statistical Analysis, Machine Learning, Data Governance, Sql, MLops, Python, Prescriptive modeling, Semantic Layers, Feature engineering, Agent orchestration frameworks, Embeddings, LLM APIs, Real-time anomaly detection models, ML pipelines, RAG, Large-scale data processing systems, AI agents

Sql, Pandas, Spark, Predictive Modelling, Python, Statistical Modelling, Data Strategy, Cloud platforms, Matching algorithms, Lift analyses, MLOps practices, Jupyter, AI agents