• Written by Azhar Aliyar, Director

When evaluating AsscherAi for a large organisation, some people object that fine tuning a model on domain data is too complex, but this objection is misplaced because it misunderstands what fine tuning actually is: the process of adapting a pre-trained model to a specific organisation's data, typically by adjusting the model's parameters to better fit the organisation's particular context. Fine tuning is not a replacement for the initial model training, but rather a way to refine the model's performance on a specific task or dataset. A purchasing manager, for example, may use fine tuning to adapt a model to the organisation's purchase order table, allowing for more accurate predictions and better decision making.

Definition and distinction

Fine tuning is often confused with retraining, but the two are distinct: retraining involves training a model from scratch on a new dataset, whereas fine tuning involves adjusting a pre-trained model to fit a new dataset. This distinction is important, as retraining can be a time-consuming and resource-intensive process, whereas fine tuning can be a more efficient way to adapt a model to a new context. The category manager, for instance, may use retraining to build a new model for a completely new product line, but fine tuning to adapt an existing model to a new supplier.

The difference between fine tuning and retraining has significant implications for how AsscherAi is used in a large organisation. By fine tuning a model on domain data, an organisation can quickly adapt the model to its specific needs, without having to invest in retraining the model from scratch. This can be particularly useful in situations where the organisation's data is constantly changing, such as in the case of a retail organisation with frequent changes in inventory and pricing.

Applicability in a large organisation

Fine tuning can be a valuable tool in a large organisation, particularly in situations where the organisation has a large amount of domain-specific data. By fine tuning a model on this data, the organisation can create a tailored solution that meets its specific needs. A shift supervisor, for example, may use fine tuning to adapt a model to the organisation's maintenance log, allowing for more accurate predictions of equipment failures and better maintenance scheduling. This can help the organisation to reduce downtime and improve overall efficiency.

In addition to its use in specific departments, fine tuning can also be used to improve the overall performance of AsscherAi across the organisation. By fine tuning the model on a wide range of data sources, the organisation can create a more accurate and comprehensive picture of its operations, allowing for better decision making and more effective resource allocation. For more information on how AsscherAi can be used in a large organisation, please visit our website.

Limitations of fine tuning

While fine tuning can be a powerful tool, it is not a panacea for all data analysis needs. Fine tuning is typically used to adapt a model to a specific task or dataset, but it may not be effective in situations where the data is highly heterogeneous or where the task requires a high degree of creativity or innovation. In these situations, other approaches, such as retraining or transfer learning, may be more effective. The IT department, for instance, may need to use retraining to build a new model for a completely new application, rather than trying to fine tune an existing model.

Furthermore, fine tuning requires a significant amount of domain-specific data, which may not always be available. In situations where the data is limited or of poor quality, fine tuning may not be effective, and other approaches may be needed. The data analyst, for example, may need to use data augmentation techniques to supplement the available data and improve the performance of the model.

When fine tuning is the wrong choice

Fine tuning is not always the best approach, and there are situations where it may be the wrong choice. One common mistake is to use fine tuning as a way to avoid retraining a model, when in fact retraining is necessary to achieve good performance. This can lead to a situation where the model is not well adapted to the new data, and performance suffers as a result. Another mistake is to use fine tuning as a way to try to adapt a model to a completely new task or dataset, when in fact the model is not well suited to the task. In these situations, it is better to start from scratch and train a new model, rather than trying to fine tune an existing one.

For organisations that are unsure about whether fine tuning is the right approach, it may be helpful to consult with a data analyst or other expert. We offer consulting services to help organisations determine the best approach for their specific needs, and to ensure that they are getting the most out of their data analysis efforts. By taking a careful and considered approach to fine tuning, organisations can avoid common pitfalls and achieve better results from their data analysis efforts.