Sendient Services
Building customised machine learning models
Building a custom machine learning model is frequently the preferred choice for organisations that have highly unique data and decision making processes. Sendient’s team of consultants and AI engineers, have the ability to design, build and improve these models, to support organisations achieve their automation goals
We break this down in to a series of steps
Understanding Your Needs: The first step focuses on understanding the specific objectives of both the organisation and the process that the machine learning model is to be designed for. This helps our team define the problem statement and identify the key outcomes expected from the model.
Data Collection and Preparation: A foundational aspect of any machine learning model is data. This can include structured and unstructured data sets, according to the models requirements. Sendient can provide guidance on how to gather relevant data, which may involve sourcing from internal databases, file systems or even public datasets. Once collected, Sendient will provide guidance on how this data should be cleaned, processed, and transformed into a format suitable for machine learning and artificial intelligence.
Model Selection and Development: Sendient will help determine which type of machine learning algorithm is most likely to achieve your desired project outcomes. This could range from relatively simple linear regression models for prediction tasks to complex neural networks for pattern recognition. The model will be developed, and will continually weigh up the balance between accuracy, computational efficiency, time and cost to maximise your return on investment.
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Training and Validation: Sendient will train the model on the prepared dataset. This process is designed to iteratively adjust the model to minimise errors or negative outcomes. In addition, the team will also implement validation techniques, such as cross-validation, to ensure the model's performance is robust and transferable to new, unseen data.
Deployment and Integration: Once the model is built and working in line with expectation, Sendient will provide guidance on how It can be deployed into your operating environment. This includes integrating the model with existing systems and ensuring it works seamlessly within your organisations existing workflows.
Performance Evaluation: After training, the model's performance will be evaluated using relevant metrics. For classification tasks, metrics like accuracy, precision, and recall will be considered, while for regression models, measures such as mean squared error might be used. These steps ensures the model meets the expected standard, and are aligned with the objectives captured in step 1.
You could build predictive models for customer behaviour, classification systems for image or speech recognition, recommendation engines for e-commerce, anomaly detection models for fraud prevention, time series forecasting for stock market trends, or natural language processing models for sentiment analysis and chatbot interactions.
Ongoing Support and Iteration: Machine learning models may often need to be adjusted, as data, processes, goals and objectives change within an organisation. Sendient is able to deliver ongoing support for model maintenance, including updates and improvements as new data becomes available or as your business objectives evolve.
The process of building a machine learning model is complex, yet the value that it delivers can transform an organisation and deliver much enhanced operational efficiency. Sendient’s team of AI consultants and machine learning engineers have years of experience in supporting organisation on this journey. To find out more about how we can help you build an AI or ML based model, please complete our contact form an account manger will respond to your enquiry.
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