MLOPS

Automating AI/ML model deployment and Setting up monitoring for the ML pipeline - Automating CI/CD pipelines to account for data, code, and model changes - Programming, working knowledge of machine learning algorithms and frameworks, and domain knowledge - Querying and working with databases, testing ML models, Git and version control, frameworks like Flask, FastAPI - Proficiency in tools such as Docker and Kubernetes - Familiarity with experiment tracking frameworks such as MLflow - Setting up and automating data pipelines using tools such as Airflow, Kafka amd Rabbitmq - Providing best practices and executing POC for automated and efficient model operations at scale. - Good to have hands-on experience using large foundation models (e.g. LLMs) and associated tool chains (e.g. langchain) and APIs to build applications, tools and workflows for production. AWS MLOPS