Even in this new age of AI, the old computer science adage “garbage in, garbage out” still holds true today, if not more important than ever. Using “ML model-ready” data can be the difference between an effective and an ineffective AI implementation.
When it comes to training effective machine learning (ML) models, engineers increasingly struggle with messy data, which poses challenges for those expected to understand and organize these datasets for AI tools.
So how can data scientists and data engineers around the world be sure that all their data is truly “ML model ready”?
Principal Enterprise Architect, Artificial Intelligence and Machine Learning, BT Group.
Unstructured and disparate data: the enemy of AI projects
The main challenge when working with unstructured and heterogeneous data sources goes back to the fact that ML models are highly dependent on the data they were trained on. If this data changes unexpectedly, it will have a huge impact on the overall performance of the model. With this in mind, understanding where your data comes from is crucial to prevent your ML models from being exposed to information of unknown origins that could lead to erroneous predictions or decisions.
To address this issue, engineers must apply dedicated data lineage and data mutation capabilities to mitigate “bad data.” The data lineage process tracks data throughout its entire lifecycle. Creating a clear audit trail of this information allows companies to monitor changes, understand data sources, and ensure ML models are running as efficiently as possible.
Another data processing technique to leverage alongside data lineage is semantic modeling. Semantic modeling helps organizations improve data quality by representing all data in a way that accurately captures its source, allowing organizations to understand its importance and intended use. This process helps organizations improve the performance of ML models by allowing them to more accurately interpret all data and process it in the most efficient way possible.
By leveraging data lineage and data mutability, ML models are built on a more reliable foundation, improving confidence in their decision-making capabilities and overall performance.
Because the performance of an ML model directly depends on the accuracy of the data used to train it, leveraging these techniques ensures that your ML models work effectively from the ground up.
The importance of considering ethics in all situations
Ethics is a crucial yet often overlooked part of the AI implementation process. Building and deploying AI safely and responsibly is a challenge every company faces, but there are a few key ways companies can address these challenges. First, they need to ensure that humans are always involved during the implementation process. This acts as an additional layer of security and allows companies to identify and address bias in their training data, while also bringing ethical judgment capabilities to the training process. These are both very important steps.
Finally, by leveraging data lineage and semantic descriptions, companies can fully understand the lifecycle of all their data and, thanks to semantic descriptions, know the additional context behind it, such as its structure and relationships with other data sets. Thus, by monitoring data lineage and leveraging semantic descriptions, assigning permissions for data usage can support compliance with data protection and management policies from the start, helping to further mitigate ethical issues.
As AI implementation becomes a key priority for businesses to streamline processes and enhance their overall products and services, it is critical that ML models are trained effectively and with ethics considered at all times. Without ethical considerations and thoughtful data handling methods, companies risk creating ineffective and unethical ML models and poor AI implementation.
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