WHAT WE DO
Machine learning
Let your machines work for you.
Wouldn't it be nice when machines can perform tasks without explicit instructions. That is exactly what machine learning is all about: learn your machine to help you. Yet developing algorithms and fine-tuning statistical models is a complex and time-consuming taks. Luckily we are there to help you!
What is machine learning?
With GenAI emerging, everything is labelled as Artificial Intelligence. Even though Machine Learning and Artificial Intelligence are related fields, they are not the same. Machine Learning is a subset of AI that specifically focuses on the development of algorithms and statistical models that enable computers to learn from and make decisions based on data. ML systems improve their performance over time without being explicitly programmed to perform a task.
Supervised machine learning
Involves training a model on a labeled dataset, where the input data is paired with the correct output. The model learns to predict the output from the input. Examples include classification (e.g., identifying spam emails) and regression (e.g., predicting house prices).
Unsupervised machine learning
The model is trained on unlabeled data and must find hidden patterns or intrinsic structures in the input data. Examples include clustering (e.g., grouping customers by purchasing behavior) and dimensionality reduction (e.g., reducing the number of variables in a dataset).
Reinforcement learning
Involves training a model to make a sequence of decisions by rewarding or punishing it based on the actions it takes. This is commonly used in robotics, gaming, and navigation tasks.
Neural networks
A set of algorithms inspired by the human brain, designed to recognize patterns. They consist of layers of nodes (neurons), where each layer transforms the input data in some way, gradually learning complex patterns and relationships. Deep neural networks (neural networks with many layers) are particularly effective in handling large, complex datasets like images, audio, and text.
How we turn your ML use-case into success.
Through our data strategy offering, we help our customers to identify valuable use-cases, even those including machine learning tasks. With our data architecture offering, we assist you in selecting the right toolbox to support your use-cases. That is rather the start of your use-case, you can now start creating value. Our experienced machine learning engineers can assist you throughout the entire process of data collection, algorithm selection, model training and evaluation, and MLOps.
We recommend the most suitable ML algorithms and techniques based on the use-case requirements and data characteristics. We develop, train, and evaluate ML models using best practices to ensure they meet the defined success criteria. Fine-tuning models can take a huge amount of time, and has an uncertain outcome. Together we time-box the development time, to optimize your effort-to-value.
We will implement performance monitoring to continuously monitor the behavior of your algorithms. We will develop strategies for model retraining to either adapt to the arrival of new data, or purely to improve the model. We do not only focus on the mathematical performance of models: in the end you build use-cases to obtain value, hence we will measure the business impact of your machine learning use-cases.
Get started today
Your ML journey can start today toegether with Dataminded!