The future of AI deployment

According to the Gartner analyst and research director Erick Brethenoux AI model deployment is one of the biggest challenges for Machine Learning engineers nowadays. Many more projects (47%) fail to even make it out of the experimental phase and into production. The whole process is difficult, takes week or even months, and requires a particular deep tech knowledge in specific areas. At Syndicai we are on a mission to change that by shaping the future of AI deployment. We are building a platform that aims to simplify the whole deployment and integration of AI models in production.  We believe that making AI more accessible and usable will allow engineers to focus on solving big problems more efficiently rather than spending time on setting up the infrastructure.

What is the problem?

In order to build a machine learning model, we need to collect, clean and prepare data, as well as train, test, and tune a model. If everything goes according to plan and the model is able to perform the desired task with high accuracy we’re done. Unfortunately, for business people, trained but not deployed model does not bring any value to a company. Despite the fact that this last step is very important, only a small percentage of AI models reach that final phase. There are several reasons for that, the main ones are as follows:

1. Computing - Deep learning algorithms require a lot of computing power not only for training but also for model inference. Clever management of those resources is a big challenge and it’s not easy.
2. Scalability - Some libraries and frameworks used by Data Scientists are not adapted to perform distributed operations.
3. Lack of resources to learn - Roughly 95% of AI courses on the internet covers only the first part of the Machine Learning workflow finishing with the trained model

A list of problems with AI model deployment is quite long.

What might be the solution?

We believe in a clever way of putting AI models into production that smoothen the whole process. As true lovers of simplicity and advanced automation, we see a huge opportunity in this area. We feel challenged to face that.

Our mission in Syndicai is to simplify the process of making AI models accessible and usable to AI developers and engineers. By cutting out the middleman and creating a platform that is so easy to use that even your grandmother can do it.

We want every developer (especially ML engineer) to be able to deploy and implement a trained AI model. Without dealing with resource management, scalability, computation, and security.

Closing remarks

Our vision is to change the way developers implement AI models into production by allowing them to easily share their work and focus on their proficiency instead of building infrastructure.

If you are working on AI deployment and want to compare notes, get in touch.