When we talk about making AI models smarter and more reliable for complex scenarios, our focus is more on improving algorithms, keeping training data quality as a secondary aspect to focus on. But is this model-centric approach really working? Are we able to improve the AI model performance by upgrading its algorithm while keeping training data static? The answer is NO.
In the model-centric AI approach, one of the most critical commodities that gets overlooked is – training data. To create more advanced AI applications that can perform well even in rare-occurring situations, the focus needs to be shifted from model architecture to the diversity and quality of training data. The more rich, accurate, and diverse your training data, the better it will perform in real-world scenarios. That’s why businesses are increasingly shifting towards a data-centric AI approach rather than a model-centric one.
Let’s understand how the data-centric approach redefines AI space and what factors businesses must consider when implementing it.
Model-Centric AI vs Data-Centric AI – Detailed Comparison
| Aspect | Model-Centric AI | Data-Centric AI |
| Primary Focus | Prioritizes the development of sophisticated models by improving algorithmic complexity and model architecture. | The focus is on enhancing the quality of training datasets through efficient data cleansing, labeling, and validation with human supervision. |
| Overhead Cost | As a large number of computational resources are required for algorithmic improvements, the model-centric AI is more expensive than the data-centric AI. | While this approach requires less computational resources, the overhead cost is significant as you need to invest in data quality management workflow/services. |
| Error Diagnosis | Errors are typically identified and removed at the model or algorithmic level (e.g., adjusting model parameters or choosing better models). | Errors are diagnosed by analyzing data quality issues, such as incorrect labels or biased data representation. |
| Time to Market | There is a longer time to market due to the iterative process of model training, testing, and tuning. | Potentially faster time to market if data is readily available for labeling or if you have a dedicated team of data labeling experts to handle large-scale annotation. |
| When It’s Most Useful | It works great when you have high-quality training data, but the AI model is not able to interpret that data efficiently, or its speed needs to be improved. | When the training data is messy, sparse, or of poor quality, and you need to focus on fixing it to train a model for handling edge cases or complex real-world scenarios. |
In simple words, the key difference between model-centric AI and data-centric AI is that the former approach focuses on improving AI’s “thinking process” (model), while the latter focuses on improving the “input” (data) the AI learns from.
Limitations of Model-Centric AI – Why Businesses Prefer Data-Centric Approach
One of the key limitations of model-centric AI that we have seen in the above table is that it needs high computational resources and is thus costly & time-consuming. Apart from that, some other shortcomings with this approach include:
- Risk of Overfitting
In model-centric AI, the risk of overfitting is high. It means that such AI systems can perform exceptionally well on the data they’ve seen and trained on but struggle with new, unseen data. And since training data is usually kept static in the model-centric AI, these systems struggle to perform in complex real-world scenarios without data fine-tuning.
- Lack of Model Explainability
The concept of “black boxes” is common in AI systems, where it is difficult to understand why and how the model makes decisions. Since the primary focus is not on improving training data quality and accuracy in model-centric AI, it becomes even harder to trace back errors or root causes in case of biased outcomes.
- Vulnerability to Data Shifts (Concept Drift)
Over time, real-world data can change, a phenomenon known as concept drift. Model-centric AI approaches often struggle to keep up with these changes as they are not continuously retrained with the updated training data. Without a robust data pipeline, even the best models may fail to adapt to evolving data distributions, leading to degraded performance.
Challenges Faced by Companies When Adopting Data-Centric AI Approach and Ways to Overcome Them
Due to the above-stated issues, businesses are shifting towards a data-centric AI approach. While this data-centric approach is more cost-effective and efficient for making smarter next-gen AI solutions, it comes with its own set of challenges. Let’s see what could be the potential roadblocks and how to overcome them for successful AI implementation:
- Data Quality
The McKinsey report reveals that 70% of surveyed organizations struggle to integrate data efficiently and quickly into AI systems. This is primarily because they find it challenging to get high-quality data for model training.
In specialized fields like healthcare or legal, data collection for AI/ML model training becomes difficult due to strict regulations (e.g., GDPR, HIPAA) and scarcity of subject matter expertise. Also, when the data is collected/combined from multiple sources, it often results in mismatched formats, redundancies, or discrepancies, complicating preprocessing and labeling for model training.
When not reviewed and updated consistently, the training data can become outdated or noisier (containing outliers, errors, duplicates, etc.), causing models trained on outdated data to underperform.
Ways to Improve Training Data Quality:
For the efficient working of AI/ML models in real-world scenarios, it is crucial that the training data be up-to-date, accurate, complete, diverse, and reliable. To ensure training data quality in data-centric AI models, you must:
- Prioritize data cleansing: The collected training data must be reviewed (through automated and manual methods) for errors, duplicates, inconsistencies, outliers, and missing information. Once the issues are spotted, it must be cleansed and updated to maintain accuracy, completeness, and timeliness.
- Enrich training data for contextual relevance: Ensure accurate data labeling by subject matter experts to add more context to training datasets. Don’t rely solely on automated tools for data annotation. While initial labeling can be done by these tools to accelerate the process, final validation must be done by experienced annotators. Implement human-in-the-loop systems to validate automated labels.
- Use high-quality data sources: Collect data from reliable and relevant websites or mediums. Before labeling the data, validate that the acquired dataset must align with the problem requirements and be free of biases. It is better to regularly update the training datasets to include fresh and relevant details for better model understanding.
- Data Security and Privacy
Maintaining data privacy and security are significant challenges for data-centric AI because the AI/ML models rely heavily on the quality and quantity of training data. As a large amount of information is fed into the AI systems (that contain sensitive details of users and organizations), the risk of data breaches is high.
Also, storing large datasets in centralized locations creates a single point of failure for cyberattacks. Collecting and using data while ensuring explicit user consent is challenging but critical to complying with regulations like GDPR, CCPA, and HIPAA.
Ways to maintain data security during annotation and model training:
- Leverage synthetic data: Use artificially generated datasets that mimic the original data while removing sensitive details to provide more diverse but privacy-free training data to AI/ML models.
- Encrypt data end-to-end: Use strong encryption methods, such as AES (Advanced Encryption Standard) and TLS (Transport Layer Security), to secure data both at rest and in transit.
- Role-Based Access Control (RBAC): Limit data access to only authorized personnel based on their roles to minimize the risk of data leaks or breaches. Also, multi-factor authentication should be used to ensure authorized access to data.
- Scalability
Since the primary focus is on training data in data-centric models, scalability is a significant challenge. As the volume of training data grows, the tasks of processing, labeling, and managing it become more complex. Pre-processing, annotating, validating, and updating training datasets demand significant time, advanced infrastructure, and skilled resources—each adding to substantial financial costs.
Ways to overcome scalability issues:
- Outsource data annotation services: The most viable way to remain cost-effective and scalable is to partner with an experienced and reliable third-party provider for data labeling services. These providers have dedicated teams of annotation experts who are proficient in using advanced data labeling tools to create high-quality training datasets. As they have access to prominent data annotation tools and high-computational resources, you don’t have to invest additionally in employee hiring, training, or infrastructure.
- Utilize active learning techniques: Incorporate active learning to focus labeling efforts on the most informative data points, minimizing the amount of data required while improving model performance.
Key Takeaway
The shift from model-centric to data-centric AI is critical for organizations to ensure model explainability, reliability, and adaptability. However, to ensure that this approach works effectively and AI/ML models perform efficiently in complex real-world scenarios, investing in data quality management is a must. By improving AI with quality data, you can support growth, innovation, and sustainability in a tech-driven competitive era.
