In the era of big data, enterprises are trying to unlock hidden value in the inherent structures of large, complex data. Machine learning offers organizations the ability to build models that explore trends, make predictions, and adapt their analysis to changing situations over time.
With wide-ranging project experience, diverse technical skills, and an entrepreneurial spirit, Ronwell offers machine learning and artificial intelligence services and solutions to address the unique needs of businesses from a variety of industries.
Services
Deep Learning: Deep learning is a form of artificial intelligence that is inspired by the human brain. By relying on synthetic structures called artificial neural networks, deep learning methods create multiple layers based on data to identify features of an object. It is a popular tool used in object detection.
Unsupervised Learning: This is a class of machine learning algorithms that operate on a certain input data without having any prior output data. The goal is to draw insights and patterns from the inner structure of a given dataset, which is called a training dataset.
Supervised Learning: To make predictions, supervised learning is highly preferred. Once you understand the trends in the data, you can use historical data to forecast scenarios that have a strong probability of happening in the future. It is a critical and most valuable asset to have in your skillset and to harness the hidden potential of your data.
Reinforcement Learning: The reinforcement learning model focuses on determining actions that can bring the highest ROI in a given environment and defending yourself against potential sources of disruption brewing in your industry. It has a vast usage preference for fields such as navigation, robotics, gaming, and telecommunications.
How Do We Work?
Our consultants partner with you to analyze whether your business should build AI capabilities. In order to figure that out for you, we follow a methodical approach with surgical precision:
Identify Problem and Collect Data
Our team sits with you to determine your business objectives and pinpoint the relevant solutions to your needs. Once we figure out what the goals are, we take a deep dive into your data. e
Collect and Clean Data
Getting raw data suitable for analysis takes a little bit of work. You need to preprocess (clean) the data to restructure it into a workable format. We basically do the dirty work of cleaning, standardizing, and labeling the original data. After that, we consolidate the data by transforming it into categories that we use for data mining. Essentially, we take data in its rawest form and refine it so that it can be used for sophisticated exploration.
Data Segmentation
We segment into three different groups: training, testing, and validation. Training data is the learning sample on which we train our model. Test data is the data that we use to improve our base model and improve its performance. Finally, we use validation data to get the model to tackle unprecedented, out-of-the-box situations, which is the most important part.
Build Models
Once the data looks solid, we start using a variety of different algorithms to build our models. At this point, we have a few available and industry-popular options such as unsupervised and supervised learning methods, depending on the size and type of the data.
Testing the Models
Picking the right model that works at the end of the day is hard. Because it’s mostly based on luck, and no one knows at the beginning which model will work. Our data scientists know that the process is based on trial-and-error and have the brainpower to think rationally about how to most effectively use the given time to optimize their models.
Deploy Models
After the A/B testing and modifications are implemented, the model is ready to be used to make the predictions and inferences that your problem requires.
