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

Tensorflow, Nlp, Jax, Pytorch, Python, ASR, TTS, Llm

Machine Learning, Deep Learning, Tensorflow, Pytorch, Opencv, Python, Computer Vision, GenAI, Prompting, LLMs, GenAI systems, OCR engines, PIL, Fine-tuning

Git, Pytorch, Docker, Linux, FastAPI, Python, Computer Vision, Deep Learning, Transformers, Hugging Face, GPU training workflows

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

Kibana, Kafka, Sklearn, Redshift, Redis, Tensorflow, Rabbitmq, Kinesis, Docker, Spark, Flask, MongoDB, Pytorch, Kubernetes, Airflow, Mlflow, Sagemaker

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

triton , Tensorflow, Jax, Pytorch, Python, Cuda, DeepSpeed, RL environments, model evaluations, FSDP

Machine Learning, Tensorflow, Pytorch, Docker, automation, Python, AWS, Git, MLops, Gcp, Azure, Kubernetes, Airflow, Infrastructure as Code, model versioning, MLflow, Kubeflow, model registries, feature stores, DevOps practices, Scikit-learn, Ai, Monitoring, CI CD pipelines, ML model deployment