AI & Machine Learning
Artificial intelligence and machine learning are changing the way we work in industry and government.
Machine learning has come into its own in recent years as computing power has become cheaper, data sets more comprehensive and methods have matured. The predictive power has been demonstrated many times in recent years from powerful recommender systems, marketing optimisation, fraud detection, document scanning and text classification. Machine learning models are excellent at recognising patterns in data which are difficult for humans to perceive.
Rapidly expanding field
The machine learning and AI fields are changing rapidly. There are a wide variety of techniques available from supervised, unsupervised and reinforcement learning to neural networks, decision trees, Bayesian networks and genetic algorithms.
A fundamental understanding of the field is essential to solving problems with machine learning. It takes a deep knowledge of mathematics, statistics, databases and computing.
Data models and infrastructure are needed to deliver on the potential of this technology. We have developed the expertise and a platform to bring AI out of the lab and into production in your organisation efficiently and cost-effectively.
Data Foundation and Collaborative System
Our Data Foundry platform provides the database foundation for these projects and our Expert Models platform provides the full end-to-end machine learning environment for training and deploying models.
We have integrated many powerful Python libraries into our Expert Models platform: Scikit-learn, PyTorch, NumPy, TensorFlow and Pandas, which allow us to utilise the latest open-source libraries in the field to get our machine learning models up and running very efficiently.
We provide the full project delivery service from concept to predictive model deployment. No two projects are the same, so we test and chose the most suitable approaches and technologies as required by the prediction challenge in each project.
The typical stages of a machine learning project involve:
- Project concept review and planning
- Data review and preparation
- Data model design and database linking or setup
- Linking and labelling target variables
- Deploying suitable computing resources and infrastructure
- Connecting the database and machine learning system
- Choosing and testing the various machine learning algorithms
- Training the model using various ensemble methods for robustness
- Deploying a trained model for real-time predictions
- Setting up a user-friendly interface to the model
Ongoing services and follow up
- Retraining the model and updating the predictive model on a routine scheduled basis.
- Project management and reporting
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Opportunities for More Effective and Efficient Microbiological Monitoring in Food Production Operations - Written by John O’Brien, Director of The Food Observatory, UK