Building the Machine Learning Model is just the first step. To monitor our predictions, offer alternatives that make our process scalable and adaptable to change, and maintain our model’s performance. In addition, it is important that we keep data regarding the execution of the pipelines, so that our processes are reproducible and that the error correction process is efficient. Using tools that support the process is essential to abstract the project’s complexity, making it more scalable and easier to maintain. The latest improvements of TensorFlow 2.0 are directed towards simplicity in model development and scaling. In this session we will look at how TFX Pipelines address DevOps and CI/CD requirements and compatibility with KubeFlow/Airflow/Apache Beam adds scalability into the mix.