ML in Production: AWS Sagemaker at Work
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.
When it comes to ML SAAS, it requires Data Engineering, ML, and DevOps expertise. While deploying a production model, several issues arise like versioning issues, and problems in the model pipeline. In SageMaker, with availability of tools for every stage in an ML pipeline, various in-house tools ease the model building and deployment.
AWS SageMaker uses Jupyter Notebook and Python with boto to connect with the S3 bucket, or it has its high-level Python API for model building.
AWS SageMaker uses integrated tools to automate labor-intensive manual processes and reduce human error and hardware costs. ML modeling components are packaged in an AWS SageMaker tool set. Software capabilities are abstracted in intuitive SageMaker templates. They provide a framework to build, host, train and deploy ML models at scale in the Amazon public cloud.
WORKING IN SAGEMAKER
AWS SageMaker simplifies ML modeling into three steps: preparation, training and deployment.
1. Prepare and build AI models
Amazon SageMaker creates a fully managed ML instance in Amazon Elastic Compute Cloud (EC2). It supports the open source Jupyter Notebook web application that enables developers to share live code. SageMaker runs Jupyter computational processing notebooks.
The notebooks include drivers, packages and libraries for common deep learning platforms and frameworks. Developers can launch a prebuilt notebook, which AWS supplies for a variety of applications and use cases. They can then customize it according to the data set and…