For any tech-enabled apps, a user-friendly interface that is not overly complex, but instead is straightforward, providing quick access to common features or commands is a must. ReshaMandi has ventured out to create a user interface that works for everyone and, on all devices. By eliminating all manual techniques, it has created an efficient automated process.
AWS S3
ReshaMandi is putting to use AWS S3 bucket or simple storage service and saving the required data in the cloud instead of saving it in the local machine.
With two AWS APIs: boto3.resource and boto3.client to data are extracted from S3.
Next, to make the process work in all systems, Docker a set of platforms with service products using OS-level virtualization is employed. This delivers software in packages called containers. One more AWS service, ECR- Elastic Container Repository
Is utilized to create a repository and make the code work.
AWS Sagemaker
Through the AWS sagemaker, ReshaMandi has created service designs to perform Machine learning tasks quickly and easily and to deploy machine learning models at any scale. This comes with its own set of benefits and applied services:
Ground Truth: A fully managed data labeling service that makes it easy to build highly accurate training datasets for machine learning.
Notebook Instance: Used to prepare and process data, write code to train models, deploy models to SageMaker hosting, and test or validate models.
Script Processor Job: Enables data analysis and understanding of the preprocessing steps needed to apply on input data and to choose the best feature engineering which train the model.
Training Job: Runs the entire population dataset. It will take input data from the specified location and validate it.
AWS Lambda function
Via AWS Lambda function, ReshaMandi is automating the tasks and eliminating the need for an entire server all the time. A serverless computer service runs the code in response to events and automatically manages the underlying compute resource effectively.
AWS Cloudwatch
Further, ReshaMandi employs AWS Cloudwatch as a monitoring service. With this technique, a forecasting model gives predictions on a daily basis. This service when used in combination with lambda function, acts as a schedule.
Utilizing AWS, ReshaMandi has automated all the manual processes, saved ample time and has successfully established a better user interface.
