Run your TensorFlow job on Amazon SageMaker with a PyCharm IDE

As more machine learning (ML) workloads go into production, many organizations must bring ML workloads to market quickly and increase productivity in the ML model development lifecycle. However, the ML model development lifecycle is significantly different from an application development lifecycle. This is due in part to the amount of experimentation required before finalizing a version of a model. Amazon SageMaker, a fully managed ML service, enables organizations to put ML ideas into production faster and improve data scientist productivity by up to 10 times. Your team can quickly and easily train and tune models, move back and forth between steps to adjust experiments, compare results, and deploy models to production-ready environments.