Industry 4.0 is synonymous with buzzwords such as machine learning, deep learning, and algorithms, which cannot be avoided anymore.
In artificial intelligence (AI), machine learning is a kind of learning that allows systems to automatically learn from and develop on their own without the need to be explicitly designed in advance. The creation of computer programmes that can access data and utilise it to learn for themselves is the subject of machine learning research and development.
Finding, extracting, and summarising pertinent data is a time-consuming process.
Making predictions based on the data from the analysis
Using probability distributions to predict particular outcomes
Using machine learning, it is possible to convert economic data into monetary value.
Adapting to specific events on one’s own initiative
Process optimization based on trends that have been identified.
Stock market research and analysis
Detection and prevention of credit card fraud.
Diagnostic procedures that are automated
Landmines are detected in data collected by sound sensors and radar.
Deep learning architecture Fox Pro’s deep learning cloud service not only helps to minimise operational expenses across the board, but also helps to handle enormous amounts of data in order to draw smart actions from that information. In particular, the ability to conduct feature recognition from large volumes of unlabeled training data is a significant advantage of our method.
Predictive analytics services are available:
Fox Pro will assist you with the development and deployment of predictive analytics solutions that bring new capabilities that will allow you to make better judgments going forward.
Acquiring knowledge about data:
The information we receive from the appropriate sources is analysed in order to have a deeper understanding of your business situation.
Preparation of data:
We clean and convert data in order to increase its quality and guarantee that it can be handled and analysed quickly and efficiently.
Construction of a model:
We develop and train models, evaluate their performance, and repeat until the target accuracy is attained.
Evaluation and deployment are two important steps:
Once you are satisfied with the assessment, we will proceed with the model installation.