Customize train 🐳 In an earlier post we went through how to run a training script using sklearn, PyTorch or transformers with SageMaker by leveraging their preconfigured framework containers. The training scripts we used were self contained, meaning they only used the respective framework and python standard library. This meant we only had to worry about uploading our data and fetching our model from s3, and deciding the instance type we wanted to use....
How to effortlessly train sklearn 📊, pytorch🔥, and transformers 🤗 models in the cloud SageMaker is a Machine Learning Operations (MLOps) platform, offered by AWS, that provides a number of tools for developing machine learning models from no code solutions to completely custom. With SageMaker, you can label data, train your own models in the cloud using hyperparameter optimization, and then deploy those models easily behind a cloud hosted API. In this series of posts we will explore SageMaker’s services and provide guides on how to use them, along with code examples....