Machine learning

Machine learning can solve many business challenges. At the heart of machine learning is data. By means of machine learning methods, data can be transformed into activity that benefits business.

Machine learning


From buzzword into practical tool

Machine learning and artificial intelligence are today’s buzzwords. There will always be technologies and trends that generate collective enthusiasm – until the hype moves on to some other technology. However, machine learning is not just a technology fad – on the contrary, the enthusiasm is quite justified. Large online shops and social networking services have been among the first to take advantage of machine learning. It makes sense, as, over the years, they have gathered a huge amount of data that can now be transformed into useful information by means of machine learning tools.

All online sellers and other operators can leverage machine learning in their operations. Many machine learning methods are open source projects and available as cloud services. Of course, expertise is required to deploy the methods.

Lamia has the know-how to find, deploy, and apply suitable machine learning methods and services – especially in online business but also more broadly.

First steps in leveraging machine learning

Where to start and what to expect from a machine learning project? Here is one approach that we have successfully used with our clients.

Step 1: Identifying a potential business challenge

A Data & Machine Learning workshop, in which:

  • you learn about the data used at your company;
  • you find potential business challenges that could be resolved through machine learning; and
  • after the workshop, methods (Lamia) and the data (client) are reviewed.

Step 2: Data audit

The purpose of a data audit is to carefully examine whether the data allows the project to progress.

  • Do we have the required data?
  • Do we have access to the data?

Step 3: Preparing the data

If the data audit gives the green light, the data is prepared and imported to an analyzing environment in the required format.

Step 4: Proof of concept

A proof-of-concept (PoC) test shows whether the chosen model and data will achieve the desired benefits. The proof-of-concept study includes teaching a machine learning model and possible cloud automatization steps (e.g. Google Cloud Platform, GCP).

Step 5: Pilot or deployment

If the PoC is accurate enough, a pilot project is launched or the model is deployed.

Looking for a partner? Let's turn data challenges into data solutions together.