Machine Learning, Data Modelling, Artificial Intelligence, Sql, Data Science, Docker, Python, Model Versioning and Experiment Tracking, Data Quality and Governance Principles, Deployment and Monitoring of ML Models, Data Engineering Fundamentals, Data pipelines and ETL ELT concepts, DevOps and MLOps practices for ML deployment and monitoring, MLOps and ML Lifecycle Management, GIT Version Control, Large Scale Dataset Processing

AI and Machine Learning algorithms, AI platforms, RAG neural networks

snowflake , Dynamodb, Data Modeling, Python, Sql, ETL frameworks, Airflow, AI-assisted development

Image Processing, Machine Learning, chatbots, deep learning neural networks, generative AI models, AWS Machine Learning

snowflake , Machine Learning, Automation, Sql, Api Development, Performance Testing, Cloud Technologies, Data Science, MLops, Data Visualization, Predictive Analytics, Python, Generative AI, AI Agents, LLMs, Business Analytics, Statistics, Claude, GPT, RAG, Azure AI

data engineering , PostgreSQL, Json, Redis, Numpy, Django, Git, Pandas, Celery, Docker, Flask, MongoDB, FastAPI, Python, Generative AI, DRF

Pytorch, Java, Tensorflow, Python, R, NLP techniques

Hadoop, Kafka, Tensorflow, Pytorch, Gcp, Docker, Spark, Azure, Kubernetes, Python, AWS, Performance Optimization, Infrastructure as Code, AI ML Technologies, MLflow, TensorBoard, CI CD, Hybrid Cloud Architecture, Kubeflow

Sql, Azure Data Bricks, Python, Web Scraping, Azure Data Factory, Azure Data Lake, Data pipelines, Beautiful Soup