Structured Data
Keep your knowledge in safe hands
Typical challenges
With our expertise, we can help you overcome structured data challenges in AI.
Aligning the technical problem formulation with business problem
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.
Validation is hard
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.
Data engineering and change management
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.
Driven by data
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.
Regression & forecasting
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.
Classification & clustering
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.
Anomaly detection
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.
Operational research & optimization
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.
High level outline of the solution
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.
Success Stories
We design AI solutions with transparency and fairness, aligning with EU AI Act guidelines and evolving regulations.
Increasing the sales hit rate to 81% at Randstad with data driven sales
We developed a scalable AI solution that allows their sales consultants to focus their time on contacting companies who have real potential for Randstad, and enabling them to build conversations based on accurate and relevant information
A smart and data driven solution for the road lighting on Flemish highways
The Flemish government and the Agency for Roads and Traffic (AWV) are collaborating with ML6 to enhance highway lighting efficiency, reducing energy consumption and light pollution. By utilizing ML6’s AI solutions, AWV can manage lighting dynamically, illuminating areas only when necessary for safety. This approach not only conserves energy and lowers costs but also protects the environment. Operators use data-driven insights for progressively automated lighting control, ensuring energy savings and reduced light pollution without compromising road user safety.
AI co-worker shortens innovation lead time by 40%
Semi-autonomous product innovation for a global FMCG manufacturer, where product concepts are developed with the help of multiple digital AI colleagues. Combined with human experts, it shortens the innovation lead time by 35-40%.
10+ years of experience in building advanced NLP solutions
Partner with ML6 for ethical AI Solutions. We bring over a decade of unparalleled expertise in crafting responsible AI solutions. Our commitment to ethical AI practices, trustworthiness, and responsible guidance in AI projects stands as a testament to our proficiency and dedication.
