Tensorflow, Pytorch, Python, MLops, Spark, Databricks, GPU infrastructure, model versioning, event tokenization, efficient attention, attention-based architectures, positional embeddings, mixed-precision training, self-supervised pretraining, gradient accumulation, automated retraining, experiment tracking, masked modeling, deep learning models, distributed strategies, contrastive learning, check-pointing, downstream fine-tuning, Production Monitoring

Java, Docker, Kubernetes, Python, TFX, AWS SageMaker, Kubeflow, Google Vertex AI, Go, MLflow, Metaflow

data engineering , Numpy, Pandas, Pytorch, Python, Testing and release, scikit-learn, Development processes, ELT data pipelines, ML research methodologies, Build processes, Production ML systems, Source control management, ML pipelines

classification), NLP (tokenization, Oops, C, C++, Os Fundamentals, Objective-c, Tensorflow, Pytorch, Swift, Python, language modeling, modern agentic and multi-agent systems, decoding, RAG architectures

Machine Learning, Kafka, Sql, MLops, Docker, Spark, Python, Kubernetes, AWS, Ai, anomaly detection, Real-time data processing, Time-series forecasting

Machine Learning, Sql, Spark, Python, Neural architectures, ML libraries, Regression, Gradient-boosted trees, ranking, Classification, Causal reasoning, Evaluation frameworks

Sql, Tensorflow, MLops, Gcp, Pytorch, Docker, Spark, Azure, Python, Kubernetes, AWS, Observability and Monitoring, Scikit-learn, API and Microservices Architecture, Vector Databases, AI Evaluation Frameworks, Feature Stores, Reinforcement Learning concepts, Model Deployment, Agentic AI Systems, Experiment Tracking

Tensorflow, Deep Learning, Java, Machine Learning, Pytorch, Keras, automation, Python, Data Analysis, R