Product development /

What kind of platform is the Peltarion Platform anyway?

May 7/5 min read
  • Reynaldo Boulogne
    Reynaldo Boulogne

In the growing AI platform landscape, it can be difficult to have an overview of which steps of the whole AI development process each one can actively help you out with. And that’s not different for the Peltarion Platform.

So in this article, we wanted to help answer what kind of platform the Peltarion Platform is and hopefully give you a better understanding of what we’re all about.

As the old saying goes a picture is worth a thousand words! So let’s save ourselves a couple of thousand words and have a look at this picture so that we’re all on the same page:

Deep learning development end-to-end workflow

This is an attempt to capture the most relevant steps, activities, and resources that are needed when working with deep learning projects. There are dozens of different ways one could draw a picture of the end-to-end development process, depending on your particular angle/point of interest, but we’ve tried to be as all-encompassing as we could.

With this picture in mind, let’s start by looking at some of the most common deep learning oriented platforms on the market:

Focus areas of common deep learning oriented platforms

As is often the case when trying to summarize a complex topic into simple visualizations/frameworks, the picture hides many details.

In particular, many platforms actually have functionalities relevant for uses beyond the boundaries shown in the picture, but the fact still remains that they are mainly a Data Management or Model management or Tracking / Version control, etc. platform with extra functionalities. 

I won’t go into detail in describing the differences and similarities of these platforms (at least not in this article), so I will let the picture speak for itself.

With this visualization, it’s easy to see that, depending on your needs in your deep learning development process, these individual platforms can save you a lot of time, headaches, and overhead costs. Being specialized platforms, the upside is that they tend to be very feature-rich and can support multiple scenarios. The downside is that they don’t help you with your complete end-to-end process, which is what you really need. The parts that the specialized platforms don’t cover either fall on you to sort out or you need to use more platforms until you have covered your end-to-end process, which can get expensive.

So what’s the alternative? You can naturally skip the platforms altogether and build a custom end-to-end pipeline yourself, which is going to be even more expensive and require tons of (expensive) experts to build and maintain. If you add on top that you probably want this pipeline to be robust, reliable, flexible, etc. it just means that it will be even trickier to build (essentially you would be trying to imitate the expertise and spending of the leading data companies of the world, i.e. Google, Amazon, Microsoft, Baidu, etc.)

Luckily there is a middle ground between these two extremes and that’s where the Peltarion Platform fits in.

The Peltarion Platform is an end-to-end / lifecycle management deep learning development platform

The Peltarion Platform is an end-to-end / lifecycle management deep learning development platform, that covers a large extent of the whole end-to-end process.

The platform has the huge advantage of unifying all the steps of the development lifecycle, which makes it easy to create, track and manage projects from the data that is used to train a model, all the way to the deployment of the trained model. Everything is in one place, nothing is ever lost and the single development environment makes it easy and faster for users to work. This is a powerful aspect that specialized platforms simply can’t match.

The downside is that by trying to cover a broader range of steps, the platform is not as feature-rich as the specialized platforms in each step, at least not for now. So is this a  drawback? Not necessarily. Unless you’re a highly advanced deep learning user that needs extremely specific features, you’re better off with an end-to-end platform that can get you started with your deep learning journey today at a fraction of the cost of acquiring multiple specialized platforms (or building a pipeline by yourself from scratch).

Why not try the platform out today? We have a free tier so you don't have to commit to anything beforehand.

  • Reynaldo Boulogne

    Reynaldo Boulogne

    With over 15 years of experience, Reynaldo has worked within the intersection of business and technology across multiple sectors, most recently at Klarna and Spotify. He is passionate about innovation, leadership, and building things from scratch. Reynaldo is also a former Vice-chairman of the Stockholm based AI forum, Stockholm AI.