Syndicai — the future of AI deployment

On the mission to simplify the process of making AI models accessible and usable to developers.

Executive summary

AI model deployment is one of the biggest challenges for Machine Learning engineers nowadays. It’s a very hard and time-consuming process that requires particular deep tech knowledge in specific areas. In syndicai we are on a mission to change that. We are building a platform that aims to redefine the way developers deploy and implement AI models. Our goal is to give developers a simple way from a trained model to actual use-case with just a couple of clicks. 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.

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

There are many more but we present just a few to give a glimpse of the current situation.

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.

Here comes the time to introduce you to our solution to the problem — syndicai platform.

Syndicai is on a mission 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 use 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.

Product

With our platform we took as our main focus in the area of AI model deployment and implementation. Our goal is to make this process smooth and fast.

No middleman, no deep tech knowledge required — you get a simple way from model to actual use-case.

syndicai AI deployment & implementation workflow
Drag & Drop your AI model to deploy and implement using various frameworks and libraries — syndicai will take care of everything whats in the middle.

Step 1: Deploy

In order to deploy a model, first you need to place two additional files in your model directory: syndicai.py which contains only python function and requirements.txt. Those files are necessary to recreate the working environment and allow to access the model by web service. When it’s done you just drag & drop your trained model on the platform. The whole process takes roughly 20min.

Step 2: Implement

Developers who want to implement a model get specially generated code snippet ready-made to copy and paste in different environments.

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.

Currently, we are at the stage of testing our alpha version of the platform, so if you feel inspired and curious about the upcoming stages or would you like to test the platform before launching — feel free to join us on slack or leave us an email.