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.
March 21, 2022
February 13, 2023
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.