Computer vision & custom model: Which AI provider to choose ?

In this article, we expose how using AI pipeline easily permits to solve complex use cases requiring OCR and text analysis.

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In recent years, within the world of Artificial Intelligence (AI), one of the most popular applications is computer vision. The main reason for this success is the great diversity of the market and needs: medical imaging, industry, transport etc. In particular, computer vision enables image recognition for all control processes, object detection and facial detection.

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What are the Computer Vision solutions?

‍Many companies use object detection to automate control processes for example. They are then faced with two choices:

1. Using pre-trained models (models already trained to classify a specific kind of "object") of the major AI providers in computer vision (Google Cloud, Amazon Web Services, IBM Watson, Microsoft Azure, Clarifai, etc.) in this case the object they want to identify is automatically recognized by these models.

2. If no pre-trained model is satisfactory, then the user will have to train a specific model for his own needs. In this case he has two possibilities:

• Create a customized solution, choose the algorithm himself and build the predictive model from scratch.
• Use an API / Console allowing the creation of a custom model automatically

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What is AutoML?

‍The last possibility is based on a technology called Auto Machine Learning (AutoML) applied to the Deep Learning algorithms (neural networks) used in vision. This service has been created to respond to the desire to democratize AI and to allow developers without a real knowledge of Machine Learning to be able to train models easily.This AI service offers the possibility to obtain fast and accurate results at a lower cost, and without the expertise of a data scientist on a subject like computer vision which uses very complex algorithms.

However, using automatic generation of the custom model also means giving up total control over the technical solution and the algorithms behind the model.Although many of the techniques used by AutoML are known, such as Transfer Learning (using existing pre-trained model algorithms to detect similar objects) and Neural Architecture Search (an algorithm for building neural networks from assembled, deleted, added blocks), the algorithm is never precisely known by the user.

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Who are the Providers? for AutoML?

‍During our study of AutoML Vision (name associated with Google, but considered in this article as generalist), we projected ourselves in the role of a company that wants to develop an image classification project for a very specific object. We embody a company that does not have an AI expert and wishing to obtain a high level of performance at a lower cost, therefore without using a service provider. So the first question that comes to our minds is, "Which provider do I choose?" A first observation appears after going through the market: only a few providers offer an AutoML Vision service.We decide to test 4 of them:

• Google autoML Vision: https://cloud.google.com/vision/automl/docs/?hl=fr
• Azure Custom Vision: https://azure.microsoft.com/fr-fr/services/cognitive-services/custom-vision-service/
• Amazon Web Services Rekognition Custom Labels: https://docs.aws.amazon.com/sagemaker/latest/dg/image-classification.html
• Clarifai: https://www.clarifai.com/train

It is on this basis that our Eden AI offer comes into play. Thanks to our in-depth expertise in the use of these different Artificial Intelligence solutions, we are able to provide the recommendation most suited to your problem, and save you a lot of time and money.

You are a solution provider and wish to integrate Eden AI, contact us at : [email protected]

This article is brought to you by the Eden AI team. We allow you to test and use in production a large number of AI engines from different providers directly through our API and platform.

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Frequently Asked Questions (FAQ)

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What is Computer vision & custom model?

Computer vision & custom model is an AI-powered capability that helps developers and businesses automate workflows, improve decision accuracy, and process data at scale without manual intervention.

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How does it work?

The process typically involves sending data (text, image, audio, or document) to an AI model via API, which returns structured results in JSON format. The model handles all the complexity behind the scenes.

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What are the main use cases?

Common applications include document processing, content moderation, data extraction, language translation, and building intelligent chatbots or recommendation systems.

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How do I get access to multiple providers?

Eden AI aggregates the best providers for this use case under a single API, letting you compare and switch between models without managing multiple accounts or API keys.

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Is it suitable for production environments?

Yes. Most AI APIs offer SLAs, rate limits, and enterprise plans suitable for production use. Eden AI adds fallback routing and centralized monitoring to further improve reliability.