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

Keras, Sql, Decision Trees, Tensorflow, Pytorch, Python, Arima, Hypothesis Testing, Statistical Analysis, Z-Test, CNTK, Great Expectations, T-Test, Regression, probabilistic graph models, MXNet, support vector machines, exponential smoothing, ARIMAX, Sci-Kit Learn, KubeFlow, BentoML, Evidently AI

AWS, Sql, Grafana, Machine Learning, MLops, Prometheus, Artificial Intelligence, Kubernetes, Python, Azure, Docker, Large Language Models, Prompt Engineering, LLMOps, Data Bricks, Agentic AI, Generative AI

Github, Apis, Tensorflow, Numpy, Git, Pandas, MLops, Pytorch, Docker, Azure, Kubernetes, Python, AWS, agentic architecture, GenAI, scikit-learn, LLMOps, Llm, distributed services, infrastructure as code

Natural Language Processing, Microservices, Devops, Machine Learning, Python, Data Privacy, Apis, responsible AI, large language models, Security, cloud environments, model-serving pipelines, data ingestion, AI services, CI CD

lifecycle management , Scalability, Python, Machine Learning, Transformers, MLOps Practices, AI Agents, Observability, Prompt Engineering, Model Deployment, GenAI Technologies

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

Databricks, Tensorflow, Pandas, Numpy, MLops, AWS, Pytorch, Python, Azure, Docker, Git, Hugging Face, LLMs, Transformers, MLflow, retrieval-augmented generation, scikit-learn, CI CD, LangChain