Optimisation Lab
Minimising the Cost of AI with evoML during an Economic Downturn
Costs of AI/ML: How Expensive Is it to Develop and Maintain AI Solutions? In the past few months, we have been closely monitoring emerging economic trends and the government’s fiscal and monetary policy decisions to better understand their impact on
How to Achieve Optimal Performance: Code Optimisation in the AI ecosystem
Boosting performance is a priority in the AI space. Businesses will not hesitate to make
Explainable Artificial Intelligence (XAI): Overcoming the Challenges with evoML
“Investor Sues After an AI's Automated Trades Cost Him $20 Million.” The headline that shook
How Can Data Scientists Write Production-quality Machine Learning Code?
Data science is a field that encapsulates a range of skills. However, in the data
Customer Churn Prediction Using Machine Learning: A step-by-step Guide with evoML
In our previous article, Customer Churn Prediction and Prevention Using AI, we pointed out the
Time Series Forecasting: Predicting Dow Jones Prices and Trends with evoML
Forecasting is a crucial component in decision-making. In order to make good decisions in the
How Can Complex Model Code Run Fast? Trade-off between model complexity and running speed
There are several concerns when selecting machine learning models for a specific task. The model
Fast Hyperparameter Tuning to Improve Model Code Performance
Hyperparameter tuning plays an important role in the process of training an optimal machine learning
What Is Imbalanced Data and How to Handle It?
Imbalanced data is a common problem in machine learning, which brings challenges to feature correlation,
Four Methods to Statistically Measure Your Data Correlation
Correlations is a measure of the association between variables. They measure to what extent one
Feature Generation: what it is and how to do it?
In our everyday life we are faced with decisions. One of the reasons why we
Feature Selection: the “why” , the “what” and the “how”
Data scientists often use Feature Selection techniques to reduce the number of features and keep
Data Quality in Machine Learning: How to Evaluate and Improve?
Introduction With data being at the heart of machine learning, it is inevitable that the