Name: Amazon Rekognition Custom Labels VS Sagemaker Image Classification & Object Detection Algorithms
Rating: 4.7 (100 reviews)

Introducing Amazon Rekognition:

Amazon Rekognition makes adding image and video analysis to your applications easy. You provide an image or video to the Amazon Rekognition API, and the service can identify objects, people, text, scenes, and activities. It can detect any inappropriate content as well.

Introducing Amazon Sagemaker:

Amazon SageMaker is a fully managed machine learning service. With SageMaker, data scientists and developers can quickly and easily build and train machine learning models and directly deploy them into a production-ready hosted environment.

Amazon Rekognition Custom Labels, you can identify the objects and scenes in images specific to your business needs. For example, you can find your logo in social media posts, identify your products on store shelves, classify machine parts in an assembly line, distinguish healthy and infected plants, or detect animated characters in videos.
With Amazon Rekognition Custom Labels, no machine learning expertise is required. You can quickly train a model by providing custom data and get recommendations using its console—everything you can do with just a few clicks.
With Amazon Rekognition Custom Labels, you can easily do image classification, object detection, model training, and custom image labeling in just a few clicks.
Rekognition is an easy way to train a model for image classification and object detection by clicking a few buttons on the console.
Rekognition training and inference pricing for the Mumbai region are

While training a model, it could scale training resources according to the data size & complexity of a model. If it’s using 1 resource on training time, the cost would be $1.00/hr; if it uses 2 resources, then the cost would be 2*$1.00/hr.
Here we can’t precisely estimate the cost of training a model because training resources and its cost could be increased or decreased based on data size & model complexity.
Rekognition does not have the functionality of incremental training. If you want to train a model on new data, you have to train that model on entirely new and old data, which will act like a new model version.
Rekognition does not provide hyperparameter tuning, PageMaker clarifies, or other features to improve model performance and monitoring.

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Image Classification:
- - The Amazon SageMaker image classification algorithm is a supervised learning algorithm that supports multi-label classification. It takes an image as input and outputs one or more labels assigned to that image. It uses a convolutional neural network (ResNet) that can be trained from scratch or transfer learning when many training images are unavailable.
  - You can train a model using the PageMaker image classification algorithm to classify images.
  - Here it would help if you were Machine Learning or Data Science expert to implement this algorithm using amazon sagemaker.
  - Manually you have to perform tasks from data preprocessing to model training and inference.
  - Here you can use hyperparameter tuning, sageMaker clarify, and other features to improve model performance and monitoring.
  - Image Classification has functionality for incremental training of a model and canary deployment.

Object Detection:
- - The Amazon SageMaker Object Detection algorithm detects and classifies one or more objects in images using a single deep neural network. It is a supervised learning algorithm that takes images as input and identifies all instances of objects within the image scene. The object is categorized into one of the classes in a specified collection with a confidence score that belongs to the class. A rectangular bounding box indicates its location and scale in the image.
  - You can train a model using a sagemaker object detection algorithm to identify objects from images.
  - Here it would help if you were Machine Learning or Data Science expert to implement this algorithm using amazon sagemaker.
  - Manually you have to perform a task from data preprocessing to model training and inference.
  - Here you can use hyperparameter tuning, sagemaker clarify, and other features to improve model performance and monitoring.
  - Image Classification has the functionality of incremental training to a model and canary deployment.

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Conclusion:

Use Rekognition Custom Label to train a model and easily classify image or object detection.
If you don’t care about model incremental training, cost, and additional configuration, then you can use Rekognition Custom Label.
Use Sagemaker Image Classification or Object Detection if you want to classify an image or detect an object and If you have a machine learning or data science expert who can train a model by doing custom configuration and model monitoring.
Use Sagemaker Image Classification or Object Detection if model performance, incremental training, and cost savings are important and if you require additional configuration like Sagemaker clarify, canary deployment, etc., to make it a strong model.
The Inference price of Rekognition is very high than the inference price of Sagemaker Instances.
Even though the GPU instance price for Sagemaker is high, it won’t take much time to train, but Rekognition will take much time to train and charge a higher amount.