Many business processes are recorded in tabular datasets like Excel sheets, relational databases, and time series. Thanks to our Machine Learning expertise, we turn your structured data into valuable insights that help you solve a variety of problems.
Predicting the future is hard, but with the right tools, we can forecast trends in e.g. energy consumption or sales volume with precision. We do this by using external data sources and taking advantage of the latest model improvements.
Labeling data records adds value to large data sets and automates actions. It involves creating different labels (clustering) and assigning them to new data (classification). This technique can be used for detecting machine failures, sales abandonment, and clustering e-commerce users.
We are experts in detecting anomalies in machine and process behavior. Our strategy is to separate "abnormal" events from "normal" behavior to gain insights into causality and explainability and use them to optimize your systems.
Even if a process works well, it can always be improved. This is where our expertise comes in. We specialize in tackling complex problems such as production planning, job scheduling, vehicle routing, box packing and more.
Discover how our expertise in Hardware & Sensors leads businesses to success
The sales effectiveness tool is currently actively used by around 10.000 sales consultants spread over 6 countries. The hit rate has increased from the historical 25% to 70%, meaning that today the sales consultants using the tool are spending 70% of their time with the right clients - clients who have actual and feasible potential for Randstad.
March 21, 2022
"Lights on highways are turned off when possible and the highways are illuminated when necessary." Based on this lighting vision of the Flemish government, the Agency for Roads & Traffic (AWV) is working with AI specialist ML6 to automatically control motorway lighting based on artificial intelligence. It will soon be possible to control highway lighting for shorter periods and smaller regions. This will have beneficial economic, environmental and social effects. The solution saves energy and light pollution is countered when the lights do not have to be switched on (or can be dimmed). Where it is required for safety, the lights are switched on. The operator can make a data-driven decision, as a first step towards a full-automatic control of lighting on the Flemish road network. This results in energy savings and a reduction of light pollution, which are key objectives of the project, are key objectives of the project, without compromising safety for road users.
February 13, 2023
By embedding artificial intelligence in its platforms, Keypoint is able to reduce fraud cases and will guarantee all home repairs are completed properly and at a fair price. Next to that, water damage claims will be prevented through the implementation of smart sensors and AI.
December 30, 2021
With our expertise, we can help you overcome structured data challenges in AI.
Before starting machine learning (ML) model training, you need to understand the business requirements and available data. This includes deciding whether the problem should be approached as a regression or classification task, or whether ranking or recommendation is required. The success of the technical implementation is ultimately determined by meeting business expectations, which we always strive to achieve.
Unsupervised learning uses tools like clustering to identify data patterns, but the results can be difficult to interpret. That’s why domain experts during development need to make sure that the outcome is accurate. It’s also tough to identify causal relationships between variables and labels may not be available in situations like fraud or predicting machine failures. However, once you overcome these challenges with our help, unsupervised learning is a powerful tool for uncovering hidden patterns and gaining insights from data.
Building a successful solution for structured data requires a lot of data engineering and change management effort. Moreover, machine learning system development can lead to hidden technical problems such as poor data quality, model complexity and deployment challenges. To create long-term value, it’s not enough to simply train an ML model. Validation, integration into existing systems, ongoing monitoring and updating are essential to deliver real value over time. We help you do just that.
The first step in any structured data solution is data collection. This can be done from a variety of sources, including internal databases, APIs, and third-party data providers.
Once data has been collected, it must be cleaned and preprocessed to make sure that it is of high quality. This includes tasks like removing missing values, handling outliers, and converting data types. Exploratory Data Science (EDA) is essential.
Feature engineering involves selecting and transforming the features in the data that are most relevant to the problem at hand. This can include creating new features, selecting important features, and scaling or normalizing features.
A machine learning model can be trained after data has been preprocessed and features have been engineered. The selection of a specific model is based on how well it performed on a holdout dataset.
Once the model has been trained and evaluated, it must be deployed into production. This involves integrating the model into existing systems and workflows and monitoring its performance over time.
Contact us to turn your structured data into valuable insights that help you solve a variety of problems