Amazon SageMaker simplifies the machine learning (ML) workflow into three steps: preparation, training, and deployment. It offers a variety of features such as Autopilot for training AI models, Clarify to detect bias, and Debugger for monitoring neural networks. AWS SageMaker Studio consolidates these capabilities, making it a powerful tool for ML development and deployment in the Amazon cloud. —

XGBoost, or Extreme Gradient Boosting, is an ensemble learning algorithm primarily based on gradient boosting and optimization principles. It builds a strong predictive model by combining the predictions of multiple weak learners, often decision trees, through an iterative process. 1. HOW IT WORKS Here’s a concise technical breakdown of how XGBoost works: Gradient Boosting…

With SageMaker, we just need to make an API call using a Python SDK. SageMaker will launch ECS instances, run model training, persist the training artifacts to S3, and then shut down the EC2 instances automatically. In deployment, another API call creates EC2 instances and networking rules to access the model over the internet. — 1. INTRODUCTION SageMaker is AWS’s fully managed service for building and deploying machine learning models in production. Developers can use SageMaker to

S3, EFS, EC2, SageMaker, DLAMI etc — With cloud deep learning, you can request as many GPU machines as needed, and scale up and down on demand. Amazon Web Services (AWS) provides an extensive ecosystem of services to support deep learning applications like SageMaker and Deep Learning Containers. Any deep learning project requires three essential resources —…