Photo by Christopher Burns on Unsplash

Today, we’re going to dive into the final steps of our machine learning life cycle. And this is where we face the reality check: How good is our current model, does it already add value to our client’s problem and is it ready to be deployed to production?

In our previous articles we covered the process of data collection & data preparation, model evaluation and model training. Now, we address the procedure of validating our model performance, getting feedback from our client and deploy the model into productive use.

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How can AI help DAM users today and how can it be a game changer in the future? In the latest episode of the DAM Evangelist podcast our CEO David Backstein and DAM expert Ulrich Leidl address many important issues that drive DAM users when it comes to AI.

Generic tagging solutions vs. custom trained neural networks

Since DAM is always about the findability of assets, the biggest help through AI can be automatic tagging of assets.

Currently, automated tagging solutions implemented in DAM offer only generic keywords. This may be sufficient as an initial tagging approach, but most DAM users have very specific assets where deeper tagging…

Photo by Mia Bruning on Unsplash

Welcome back to our blog series about machine learning projects. Are you involved in planning an AI project? Then you’re in the right place. We explain all the project phases in a series of blog posts.

AI projects are usually carried out in a cyclic process. In previous articles we already talked about data collection and data preparation as well as model evaluation.

Photo by Tolga Ulkan on Unsplash

If you’re reading this you’ve landed straight in our blog post series about machine learning projects. We know that the implementation of these projects is still a big mystery for many of our customers. Therefore, we explain the phases of AI projects in a series of articles.

AI projects are usually carried out in a cyclic process. Our previous article dealt with the first two important phases of the cycle, data collection and data preparation. Today, we’re going to dive into the topic of model evaluation, which is a crucial part of phase 3 in our life cycle. …

Photo by Drew Graham on Unsplash

Welcome back to our blog post series where you’ll learn how we run machine learning projects. In the first part of this series you’ve already learned about the different phases of the project lifecycle. Today, we take a closer look at the first two very important phases: data collection and data preparation.

You should definitely read on if you plan to implement AI-supported tasks in your company, regardless of whether you are a project manager, engineer, or decision-maker.

Machine learning projects are on everyone’s lips, but from customer projects we know that the implementation of AI projects is a mystery to many. That’s why we will show you how the life cycle of our machine learning projects looks like in a series of blog posts. Our target audience for this series are project managers, engineers, decision makers, and everyone else planning an AI project.

In this first part of our series we’re going to briefly touch upon the single project phases. We’re also going to discuss special challenges we face in the field of computer vision. …

Photo by Malte Wingen on Unsplash

If you have a lot of image data to manage, then you know: identifying and avoiding duplicate images is the key to maintain the integrity of your image collection. Depending on which detection technique you choose, this can be error-prone or not applicable to large volumes of image data.

So, what is the best technique for detecting duplicate images? It always depends on your image collection and your requirements. How large is your collection? Do you want to detect exact duplicates only or also near-duplicates? Can the detection run in background or must it work in real-time?

Today, we’re going…

A few weeks ago we released our latest Visual Search technology. It contains everything we’ve learned over the past years and brings a massive improvement to the visual search experience. Now, we have integrated our high performance image search engine in our online demo, so everyone can use it.

Powered by our latest general purpose image search AI, you can upload and search your own image collection or use the prefilled demo collection.

The Hardest Challenge — Real World Imagery

Our prefilled demo contains Creative Commons photos from the Flickr community. It is a really tough task for an image search engine to handle these images and…

We’re very excited to finally share our newly designed website with you. It was time for a refresh, we wanted to make our website more user-friendly and easier to navigate. Thanks to a clear structure visitors now have better access to information about our product and services.

Our goal with this website relaunch is to offer our current and prospective customers a quick and easy way to learn everything about our product pixolution flow. …

Photo by Runze Shi on Unsplash

We will soon be spending some time in the office again, even if it is with distance and in alternating shifts. In times of physical distance and hygiene regulations, however, we should not forget how our daily work can continue to be environmentally friendly. We at pixolution strive to make our ecological footprint as small as possible. Below we have put together some best practices from our experience that are really easy to implement.

1. Go paperless

Avoid printing where possible. Only print where it’s really required to have paper documents. We hardly print at all, our office is 99% digital. Our internal…


Visual Search AI. High performance image search for your website or app.

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