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Data Engineering

The unmistakable need for datafication

Since the term big data started this big wave of everyone turning to data as a resource, many companies jumped on board. But it’s easy to utilize data when you can precisely point it out or quantify it. What happens with all the events that you previously couldn’t pinpoint? This

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machine unlearning

Machine unlearning – a new frontier

Since there is so much talk about machine learning and ways it could be utilized, it is time to mention the exact opposite of it. We have mentioned so many times that the ML model is only as good as the data it was trained on. But what happens when

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data poisoning

Data poisoning – an unexpected foe

Recently, there has been a big buzz around the downsides and negative effects of AI and ML. This has sparked the conversation on adversarial ML and adversarial attacks. Alongside the usual types of attacks, one type has stood out, especially in consideration of copyright infringement. Data poisoning has been making

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continuous intelligence

From continuous intelligence to composite AI – a mix for success

Leveraging business intelligence to make decisions has been the core of any company that somehow wants to utilize data. An approach to the ever-growing data volume has been to integrate BI systems into everyday operations. Which is perfectly fine and might work for some. The issues appear when data velocity

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multimodal ML
Data Science

Utilizing multimodal ML in a fast-shifting world

AI and machine learning are both terms that we so freely use. But, what many do not understand is that there are so many sub-areas of these disciplines. And we use them all interchangeably, which would be misleading. ML, in itself, can be divided into supervised, unsupervised, semi-supervised, and reinforcement

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federated learning
Machine Learning

What can we learn about federated learning?

We all know that for machine learning you have to train models on data before actually having fully functional systems that derive results. The same goes for AI. The traditional approach is to collect data, prepare data, train models, and deploy them. But, considering the amount of data and devices

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