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 Data augmentation is a technique that can increase the size and diversity of the training data by applying various transformations to the original images, such as inversions, translations, rotations, scaling, cropping, flipping, and color variations. Data augmentation can help improve the performance and generalization of image classification models by reducing overfitting and introducing more variability to the data. Data augmentation is especially useful when the original data is limited or imbalanced, as in the case of the company’s problem. By augmenting the training data for each item using image variants, the company can build a more robust and accurate model that can recognize the items on the store shelves from different angles, positions, and lighting conditions. The company can also iterate on the model by adding more data or fine-tuning the hyperparameters to achieve better results.

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The Effectiveness of Data Augmentation in Image Classification using Deep Learning

Data augmentation for improving deep learning in image classification problem

Class-Adaptive Data Augmentation for Image Classification

In a classification model, features with significantly larger scales can dominate the model training process, leading to poor performance. Normalization scales the values of continuous features to a uniform range, such as [0, 1], which prevents large-value features from disproportionately influencing the model. This is particularly beneficial for algorithms sensitive to the scale of input data, such as neural networks or distance-based algorithms.

Given that the problematic features are informative and representative of the target distribution, removing or bootstrapping these features is not advisable. Normalization will bring all features to a similar scale and improve the model's inference accuracy without losing important information.

The solution that will meet the requirements with the least amount of customization to transform and store the ingested data is to use Amazon Kinesis Data Analytics to read and aggregate the data hourly, transform the data and store it in Amazon S3 by using Amazon Kinesis Data Firehose. This solution leverages the built-in features of Kinesis Data Analytics to perform SQL queries on streaming data and generate hourly summaries. Kinesis Data Analytics can also output the transformed data to Kinesis Data Firehose, which can then deliver the data to S3 in a specified format and partitioning scheme. This solution does not require any custom code or additional infrastructure to process the data. The other solutions either require more customization (such as using Lambda or EMR) or do not meet the requirement of aggregating the data hourly (such as using Lambda to read the data from Kinesis Data Streams). References:

1: Boosting Resiliency with an ML-based Telemetry Analytics Architecture | AWS Architecture Blog

2: AWS Cloud Data Ingestion Patterns and Practices

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4: AWS IoT Data Ingestion Simplified 101: The Complete Guide - Hevo Data