Machine Learning

Machine Learning Enabled Transformation

Machine Learning has emerged as the most applicable area of Artificial Intelligence driving transformations in processes, decision-making, computer vision, natural language processes and automation.  Using an organisation's data machine learning creates algorithms that represent new learning from that data only achievable by machine processing.  These new algorithms can then be applied through solutions to drive performance and automation.

 

Data Engineering

 

Machine learning projects have to understand the data available which may come from many sources.  This data may need labelling and will usually need data manipulation in order to be usable by machine learning models.

Machine Learning Engineering

There are many machine learning techniques available from traditional ML, deep learning and reinforcement learning.  Choosing the most appropriate techniques and then measuring their performance for the solution is a critical area.

 

Traditional Machine Learning

Prediction and categorization using ML techniques such as Linear Regression,  Decision Trees, Naïve Bayes, Random Forests and Ensemble techniques.

Deep Learning & Reinforcement Learning

Deep learning ML techniques generally use labelled or unlabeled data applied to neural networks to create performant algorithms for deployment to advanced use cases like image classification, computer vision and natural language processing.

Machine Learning Operations

Once a machine learning algorithm has been developed then it will often need to be incorporated into the organisation's processes, systems or products and maintained.