What it takes to deliver Machine Learning

Many address Machine Learning projects as black box solutions or product delivery.
However, Machine Learning projects delivered rightly is much more than that.
It gives you insight to whether you retrieve the right data, whether your hypotheses on patterns are true, and so much more.

What is needed to deliver machine learning

Big Data vs Right Data

We have talked about it for years now. Consultancies have been saying for a long time that we need it. Maybe you are even aiming towards it in your strategy?
What exactly is Big Data? and how did we get to talk so much about it?
Before reading further, try answering the following question - no cheating: How many V's defines Big Data?

Big Data is not Right Data

Estimating the Remaining Useful Life of a Water Pump

In this blog post, we will talk about the benefits of using Remaining Useful Life predictions as a main driver for planning maintenance and detecting wear and tear before problems arise. There is a high potential of both reducing total costs on maintenance and spare parts, but also on increasing the total operational uptime, by using data when planning maintenance intervals.

Diagram showing the predicted and true RUL of the water pump

Different approaches towards predictive maintenance

In this blog post we will go through the different approaches to predictive maintenance. Right data is always the foundation and different types of machine learning algorithms can be used depending on the current situation and the problem/value we are aiming for.

Different breakdowns.

Is it Worth Reducing Unplanned Downtime with Predictive Maintenance?

According to the Danish Maintenance Association (DDV), machinery maintenance is estimated to cost Danish manufacturing companies DKK 25 billions each year. Additionally, it is estimated that there is a globally cost reduction of $240 - $630 billions in 2025, thanks to the implementation of predictive maintenance [1].

image of pipes

Myth Busting AI

There are many myths regarding Artificial Intelligence (AI) and Machine Learning (ML). As with other new technologies some are untrue and some are not. The myths that are untrue often create noise impairing us from making the right decisions about this new technology. In this blog post we will try to myth bust some of the most common myths within AI.

checklist with yes and no marked

Why is Retraining so Important

When we help customers use their data by utilizing machine learning we sometimes get asked “why is re-training important?” This is natural as in software development you can often hear people say: “When the code works you should let it be” or “if it aint broke don't fix it?”

Machine learning formula