As you probably know, the workflow of machine learning, especially in large projects, involves complex steps the way from data preprocessing to deploying the model. The typical phases include data collection, data pre-processing, building datasets, model training and refinement, evaluation, and deployment to production. All these steps bring along their own set of challenges. Therefore these pipelines should be carefully designed and handled. The Amazon SageMaker service removes the heavy lifting from each of the steps in the workflow to make it easier to develop high-quality models.
It gives you complete access, control, and visibility into each of the steps. You can quickly upload data, create new notebooks, train and tune models, move back and forth between steps to adjust experiments, compare results, and deploy models to production all in one place. What you hear now about SageMaker, it’s just the tip of the iceberg. It also offers features such as SageMaker Marketplace where you buy and sell Amazon SageMaker algorithms, Automated Machine Learning (AutoML) that provides a black-box solution to machine learning problems by selecting the right way of processing features, choosing an algorithm, and tuning the hyperparameters in your pipeline, and many more.
With Sagemaker, you have the option to either use one of the built-in machine learning algorithms from the SageMaker marketplace or create your own machine learning algorithms. In a Medium post, I tell you about everytıng you need to know to create your custom machine learning algorithm and then train and deploy it on Amazon. To learn how to create the workflow in the image and get more information, please visit the following link:
