Even in this new age of AI, the old computer science adage of “Garbage in, garbage out” is still relevant, if not more relevant, than ever before. Using data that is “ML model ready” is the difference between effective and ineffective AI implementation.
When it comes to training effective Machine Learning (ML) models, engineers are increasingly battling messy data. This creates a challenge for those who are supposed to understand and organize these datasets for AI tools.
So how can data scientists and data engineers around the world ensure that all data is truly ‘ML model-ready’?
Principal Enterprise Architect, Artificial Intelligence and Machine Learning, at BT Group.
Unstructured and heterogeneous data: the enemy of AI projects
The biggest challenge when dealing with unstructured and heterogeneous data sources is that ML models are heavily dependent on the data they are trained on, and if this data were to change unexpectedly, it would significantly impact the overall performance of the model. With this in mind, it is crucial to understand where your data comes from to avoid exposing your ML model to unsourced information that could cause it to make incorrect predictions or decisions.
To help combat this problem, engineers should enforce a dedicated data lineage and data change management function to help mitigate “bad data.” A data lineage process involves tracking data throughout its entire lifecycle. By creating a clear audit trail of this information, companies can track all changes and understand the data source to ensure ML models are operating as efficiently as possible.
In addition to data lineage, semantic modeling is another data processing technique that should be leveraged. Semantic modeling allows organizations to improve the quality of their data by representing all data in a way that accurately captures its source, allowing you to understand the meaning of the data along with its intended use. This process allows organizations to make more accurate interpretations of all data and ensure that it is processed in the most efficient way possible, leading to improved ML model performance.
By leveraging data lineage and data modification features, ML models are built on a more reliable foundation, making decision-making capabilities and overall performance more reliable.
How well an ML model performs is directly dependent on the accuracy of the data it is trained on. By leveraging these techniques, you ensure that ML models are effective down to the core.
The Importance of Considering Ethics at Every Step
Ethics is a critically important, yet often overlooked, part of the AI implementation process. Building and deploying AI safely and responsibly is a challenge for all businesses, but there are a few key ways that companies can address these challenges. First, organizations should ensure that a human is always involved in the implementation process. This acts as an additional layer of security and allows companies to identify and address biases in the training data, while also bringing ethical judgment to the training process, both of which are extremely important steps.
Finally, by leveraging data lineage and semantic descriptions, companies can fully understand the lifecycle of all data and have the additional context behind it, including its structure and relationships with other datasets, thanks to semantic descriptions. Therefore, monitoring data lineage and leveraging semantic descriptions can support compliance with data protection and management policies from the start by assigning permissions for data usage – further helping to mitigate ethical issues.
As AI implementation becomes a major priority for businesses looking to streamline processes and improve overall products and services, it is vital that their ML models are trained effectively and that ethics are considered every step of the way. Without ethical considerations and thoughtful data handling practices, businesses risk creating ineffective and unethical ML models that lead to inadequate AI implementation.
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