Machine Learning
A branch of artificial intelligence where systems learn from data and recognize patterns without being explicitly programmed for each task.
What is Machine Learning?
Machine learning is the field within artificial intelligence focused on developing algorithms that automatically learn from data. Instead of programming a system for every possible situation, a machine learning model is trained on large amounts of data, after which it independently recognizes patterns and can make predictions. This is the technology underlying speech recognition, recommendation systems, and predictive maintenance.
How does Machine Learning work?
A machine learning model is trained by feeding it large amounts of labelled or unlabelled data. The algorithm identifies patterns in this data and builds an internal model that can make predictions about new, unknown data. The most important factor in machine learning is the quality of the training data: the better and more varied the data, the more accurate the model. After training, the model is validated and optimized before going into production.
Example
A Wabber client manages a large warehouse and regularly faces stock shortages at unexpected moments. By applying machine learning to historical order, sales, and seasonal data, Wabber's WMS can predict which products will soon sell out. The system automatically generates order suggestions, ensuring the client always has sufficient stock without maintaining overstock.
Why is Machine Learning important?
Machine learning enables organizations to make data-driven decisions and optimize processes in ways that are not possible with traditional programming. It can discover hidden patterns in large datasets, detect anomalies, and predict future trends. For businesses in logistics and industry, this directly translates into lower costs, fewer errors, and faster response times.
Related solutions
Frequently asked questions
What is the difference between AI and Machine Learning?
AI (artificial intelligence) is the overarching field concerned with having computers perform intelligent tasks. Machine learning is a specific branch within AI, focused on systems that automatically learn from data. Not all AI uses machine learning, but machine learning is one of the most powerful methods within AI.
What data do I need for Machine Learning?
You need structured, historical data relevant to the problem you want to solve. This can range from sales data and sensor measurements to customer inquiries and logistics data. Wabber helps inventory and structure your data to determine whether machine learning adds value for your situation.
Does Wabber use Machine Learning in their products?
Yes, Wabber applies machine learning within various solutions. Examples include predictive inventory analysis in our WMS, anomaly detection in logistics data, and automatic classification of incoming requests. Our own AI cluster with 128GB VRAM makes it possible to run models locally, fully hosted in the Netherlands.
Related articles
LLM: from standalone chatbot to knowledge architecture
Most AI implementations fail not because of the technology, but because of the architecture around it. A chatbot without a structured knowledge source gives unreliable answers. That costs trust: internally and externally.
Read more →
n8n: Process Automation with AI
Manual workflows cost time and break at scale. An RMA request that is manually assessed, calculated and confirmed easily takes an hour. Automation brings that down to seconds.
Read more →
AI-Readiness Scan
Scan in 2 minutes - discover where you stand.
Ready to put your data to work?
Schedule a no-obligation 30-minute session. Discover how private AI and tracking systems measurably improve your operation.