An introduction to GitOps, a paradigm or a set of practices that empowers developers to perform tasks that typically fall under the purview of IT operations. GitOps requires us to describe and observe systems with declarative specifications that eventually form the basis of continuous everything.
ML Ops workshop: Tensorflow Extended running on Airflow
Key concepts of TensorFlow Extended (TFX) and develop the skills to run TFX workflows on Apache Airflow In this workshop, we’ll dive into TFX, a tool built for consistent and reliable deployment of TensorFlow-based models to production. In practice, this means that TFX allows you to follow MLOps best practices with model versioning, data validation, metadata management, performance monitoring, serving and more. For this session, we’ll explore the key concepts of TFX and teach you how to run TFX workflows on Airflow. Use the orchestration system like Apache Airflow or Kubeflow to execute workflows as directed acyclic graphs (DAGs) of tasks. At the end of this workshop, you will explore the key concepts of TFX and teach you how to run TFX workflows on Airflow, hosted on the cloud. It will demonstrate the end-to-end workflow and steps how to analyze, validate and transform data, train a model, analyze and serve it.
Renald Buter Chief Operations at GoDataDriven
Julian de Ruiter Machine Learning Engineer at GoDataDriven
Roman Ivanov Machine Learning Engineer at GoDataDriven