Deepomatic or AWS Sagemaker : Which one to choose ?

Deepomatic vs AWS

According to most research firms such as Gartner, Forrester or MarketsAndMarkets, the global computer vision market is set to reach a total of USD 20 billion by 2025. The market is therefore in full expansion, and the interest of companies is also growing. Based on this projection, we wanted to find out which computer vision platforms are best suited to the needs of businesses. In this article we will focus on the differences between Deepomatic and AWS Sagemaker as Enterprise Computer Vision Platforms.

Sagemaker was launched by Amazon in late 2017, while Deepomatic was founded in 2014 with offices in New York and Paris. Today Sagemaker focuses exclusively on the developer-first crowd while Deepomatic aims at broadening the audience and enabling anybody to developer computer vision applications.

Focus on AWS Sagemaker

Amazon Sagemaker hyperparameter tuning page.

For this article, and for all from this series, we have chosen the 6 market leaders and to evaluate them, we set up the same three different projects on each platform. This gave us a sense of the features each platform provides and the associated shortcomings if any. Then we computed the model performance which gave us a good insight into the viability of using the platform for production-level business applications. If you want to read in detail our methodology, please click here.

Amazon is part of those leaders and have a very practical approach. It provides you with the basic elements, but keep in mind that you will need a lot of coding to assemble them. When we explored the plateforme and build a model on it, we usually needed to write a bit of code to import data, set up the different projects, retrieve performances, etc. This accounted for less than 100 lines for all the other providers. With Sagemaker we have to write a whooping 1038 lines, a x10 increase. Right of the bat, if you’re not a developer it’s better to stay away from Sagemaker. On the other hand, they provide integrated Notebooks to make it easy to code directly in their environment.

Besides, Sagemaker embraces a very sequential approach. First, code a simple annotation interface and send it out to a private workforce or Amazon Turk. Then use this data to train a model or perform automatic training. Finally deploy it in the cloud or compile it to deploy on edge.

Unfortunately, today you need to be able to quickly iterate between all steps in order to reach satisfactory business levels. We found this very hard without an integrated system. For instance you have no built-in gallery view for your images and annotations. Same thing for model performance, if you want to go beyond the most basic metrics, you will need to code it out yourself.

Amazon Sagemaker targets developers, providing finer control on some basic elements but requiring a fair amount of coding to get it working. This leads to the most complex platform we have tried, with some good results if properly configured.

And now, Deepomatic

Deepomatic project overview page.

Deepomatic is at the other end of the spectrum. Here, the value proposition is to enable the largest possible audience to create and deploy Enterprise computer vision applications. This means providing customers with a one-stop platform where everything is integrated, making it as easy to use as possible while promoting industry best practices.

Practically Deepomatic provides an easy-to-use annotation interface deeply linked to the model training. This means models are used to speed up annotations with active learning, but also to review existing annotations with error spotting, this alone can reduce annotation errors by up to 10% according to our latest tests.

Training is performed seamlessly with a few clicks and a full-featured performance dashboard is then used to analyze the model and identify potential improvements.

Unfortunately, training a model is not the end of story when it comes to Enterprise applications. You then need to be able to package your model, chain them to form complex applications, version and monitor them while being able to deploy them either on public cloud, on premises or at the edge. All of which are built-in capabilities of the Deepomatic platform.

Only then you can focus on closing the loop, automatically sending interesting images back to the platform to improve model performance in a virtuous circle. Finally, Deepomatic provides a built-in monitoring dashboard to follow day-to-day field operations and an analytics dashboard to perform BI analysis on long-term business trends.

Deepomatic is the go-to-platform if you want to be able to address your whole enterprise applications lifecycle from a centralized place with built-in industry best practices and state of the art models. This is the most feature-rich platform while at the same time requiring the least amount of coding and development skills.


In conclusion, AWS Sagemaker is a platform dedicated to developers. It allows you to code your own programs to explore beyond the proposed possibilities and gives good results if you put time into it. If, on the contrary, you wish to use a centralized platform that accompanies you from start to finish of your project, easily and without coding, you should use Deepomatic . 

If you want to know more about the large-scale projects Deepomatic has carried out with its partners, click here.


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