Api Development, Pyspark, Data Modeling, Sql, Data Quality, Pandas, MLops, Databricks, Python, experiment tracking, LLMOps, CI CD, performance considerations, model registries, evaluation frameworks, LLM AI frameworks

C, Tensorflow, Azure ML, Pytorch, Docker, Kubernetes, Python, scikit-learn, TFX, MLflow, GitLab CI, GitHub Actions, AWS SageMaker, Kubeflow

bedrock , RDS, Phoenix, Dynamodb, Grafana, Tensorflow, Lambda, Pytorch, Cloudwatch, Batch, Sqs, Flask, Api Gateway, AWS, Airflow, Nanny ML, MLflow, SageMaker, Fargate, Fast APIs, Kubeflow, Evidently

Python, AI ML testing, CI CD pipelines, MLOps best practices, Automated testing in DevOps environments, Large-scale distributed AI systems

Tensorflow, Jenkins, Devops, MLops, Pytorch, Python, ML Engineering, Airflow, Scikit-learn, CI CD, GitLab CI, GitHub Actions

Tensorflow, Pytorch, Scikit-learn, Kubeflow, MLflow, Airflow

Docker, Kubernetes, Python, ML Ops, LLM Ops, CI/CD

Prometheus, Kafka, Grafana, Tensorflow, Pytorch, Docker, Terraform, Python, AWS, Jenkins, Azure, Kubernetes, MLflow, Microservices architecture, Kubeflow, MLOps tools, Data pipelines, Machine Learning lifecycle, DevOps practices, Scikit-learn, cloud platforms, SageMaker, GitLab CI, Argo CD, CI CD tools, AI ML workflows, ML frameworks