AI & ML

Artificial Intelligence and Machine Learning solutions are increasingly applied for real-life scenarios in enterprise data analytics.

Predictive modelling, full or partial decision automation, data quality improvements, fraud detection, customer segmentation, smart accounting are some of the common use-cases that we help our clients with.

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A typical AI/ML implementation project generally consists of several parts.

Business understanding

Defining well the problem to be solved or opportunity to be developed provides not only the project scope, but also helps better understand the objectives of your AI initiative and what the successful completion of the project looks like.

Data understanding

Data collection from all the relevant sources, data exploration, assessment of its quality and integrity.

Data preparation

During this step the data is cleaned (inconsistencies and errors are addressed) and formatted. The data can also be transformed or preprocessed otherwise, as required.

Modeling

Finding the right algorithm/combination of algorithms for the task at hand, training and evaluating performance of the model.

Evaluation

Evaluating the results of the AI project, including the models, findings and conclusions generated. Here the team is able to see whether the produced results meet the business objectives and requirements, and identify areas for further work and improvement.

Deployment

When the solution is deemed to be ready it is deployed for production use. During this phase the results of the data mining project are implemented. That also includes any recommendations or decisions that have been made based on the results.

Monitoring & Improvements

It is crucial to see the model does not degrade over time - it needs consistent monitoring, and possibly retraining and changes. Monitoring the model is the way to make sure it stays relevant and accurate.

Using AI for data analytics has potential to improve various aspects of company operations. But data fit for and generated by the normal business processes is not the same thing as data fit for AI. Having conducted a few real life AI project implementations, we are familiar with underwater rocks - and the ways to overcome them.