Active learning helps AI teams cut labeling costs by teaching models to focus on the most informative data first. It shows that smarter sampling, not just more data, can deliver faster training, higher accuracy, and better ROI across real-world AI projects.
AI models run on training data; however, the collection and labeling of high volumes of training data is costly. Humans take time to annotate; therefore, most datasets are expected to be redundant or of low value. As a result, the model will spend a lot of time learning from examples it already knows rather than the examples that will improve its performance.
The data labelling market is expected to grow tremendously. Data Insights Market shares that the figure is expected to touch 28,300 million by 2033! Conversely, a report by sapien AI in 2024 reveals that data preparation, including data labelling, consumes up to 80% of total AI project time, leading to staggeringly high costs.
That’s where active learning changes the equation.
Instead of labeling all data upfront, the model trains in iterations – a small batch of AI training data, identifies the most informative samples, and requests human review for the samples it is unsure of. This human-in-the-loop AI approach enables teams to label smarter, not harder.
The Payoff
Significant payoffs include fewer labeled examples, faster iterations, and sometimes similar or improved accuracy. In subsequent sections, we’ll examine how this occurs in practice and discuss seven active learning strategies that make AI training data more cost-effective and accurate.
7 Active Learning Strategies That Teams Can Implement Today
Once teams understand the rationale for active learning, the next logical question is how to implement it.
Most teams begin by testing various query strategies to determine which strategy best fits their data and labeling processes. The following are seven practical active learning strategies that can help teams improve the performance of their models while controlling their costs:
1. Uncertainty Sampling
The first and most well-known active learning strategy is uncertainty sampling. In an active learning pipeline, the model selects the data points it is least certain about. Typically, this involves selecting data points with the lowest predicted probability or highest entropy and sending them for labeling.
In particular, these data points provide the greatest amount of information, especially during early stages of training. While simple and effective, uncertainty sampling can become “noisy” in datasets that contain outliers and/or class imbalance, so a simple filtering step is typically included.
2. Query-by-Committee
Query-by-committee is an alternative to uncertainty sampling that does not utilize a single model. Instead, multiple models are trained and model disagreements are measured. When multiple models disagree on a label, it is likely that there is additional information available.
Therefore, the contentious samples are sent to humans for labeling to enable the system to achieve a clearer consensus. Query-by-committee is more robust than uncertainty sampling; however, it requires additional computational resources. Query-by-committee is often used after an initial model has reached a level of maturity sufficient to justify the added expense.
3. Diversity Sampling
Another form of uncertainty is redundancy. Diversity sampling focuses on selecting diverse data points from the dataset that are different from each other in the embedding space. Diversity sampling ensures that the labeled dataset includes a wide variety of real-world cases.
Diversity sampling is particularly beneficial in AI training data pipelines that are prone to creating near-duplicate data (e.g., image recognition, document classification). Combining diversity sampling with a simple quality filter prevents the system from labeling uninformative edge cases.
4. Core-Set Selection
Core-set selection formalizes diversity as a more mathematical process. Core-set selection aims to select a smaller set of data that is representative of the larger unlabeled pool. Core-set selection is highly beneficial for “cold starts,” i.e., when teams do not have a sufficient number of labeled data points.
Additionally, core-set selection can aid in periodic updates to maintain a balanced representation of the classes. Core-set selection is one of the most efficient methods of maintaining a representative training set without having to label an excessive number of data points.
5. Density-Weighted Uncertainty
When uncertainty and representativeness are combined, they produce density-weighted uncertainty sampling. Density-weighted uncertainty sampling focuses on data points that are uncertain and located within dense regions of the data distribution.
Density-weighted uncertainty sampling provides the greatest benefit to the model by enabling it to learn from data points that are both challenging and relevant, and not simply random outliers. Density-weighted uncertainty sampling optimizes both exploration and stability
6. Expected Error Reduction
The “expected model change” (also called the “expected error”) is a form of long-term thinking. Unlike the previous two types of active learning (i.e., selecting the next “confusing” example), this type of active learning selects the data point that will lead to the greatest amount of future model improvement or changes in the model’s parameter values.
This type of active learning requires significantly more computational resources; however, it is typically used in applications where there is a need to produce a substantial number of meaningful improvements with each labelling round (e.g., in regulatory and/or high-stakes applications). Examples include medical imaging and self-driving vehicles.
7. Pre-Trained Models for Active Learning
Another method to enhance active learning is to leverage pre-trained or foundation models.
As described previously, foundation models provide pre-trained embeddings, which allows for a more intelligent selection of the next example for labeling. With these pre-trained embeddings combined with a confidence-based selection or weak supervision, the number of human labels required can be greatly diminished. This method provides a good option for those domains that have limited amounts of labeled data and works particularly well with RAG (retrieval-augmented generation) and semi-supervised methodologies.
When To Use Active Learning (And When To Skip It)
How Active Learning Reduces AI Training Data Costs
The primary motivation behind implementing active learning is to increase the efficiency of the overall process. Many organizations report that they are able to achieve a 20-60% reduction in the number of labelled examples needed to achieve the same levels of model performance compared to passive learning systems.
However, the exact range of savings depends on the specific application and the effectiveness of the methodology employed. What is common to all of them is that the greater intelligence applied to the sample selection process, the fewer labelled examples are required to achieve strong model performance.
In addition to measuring the model performance, organizations should also monitor the following metrics to understand the actual return on investment for the labeling process:
- Progress towards reaching the desired model performance (measured through learning curves).
- Cost per labelled item.
- Frequency of achieving the first usable model.
- Annotation review times.
- Relabelling frequencies.
- Labeler agreements.
- Drift alarms.
These operational metrics indicate whether the active learning pipeline is functioning effectively.
Organizations should also relate the benefits achieved through active learning to business outcomes, including (but not limited to):
- Lower production defect rates.
- Faster agent response times.
- Lower false positive costs.
This is where the benefits become evident throughout the entire organization.
Common Pitfalls to Avoid
All active learning pipelines can suffer from degradation unless monitored regularly. Here are some pitfalls to avoid.
- Selecting based solely on uncertainty can introduce noise into the labelling process. Therefore, use a combination of diversity and density-based sampling to prevent this.
- Do not neglect rare classes. If rare classes exist, apply quotas or cost-sensitive rules to maintain a balance between the classes.
- Each cycle of learning should contain quality assurance measures to ensure that the data being used for learning is accurate. Use gold standard reference sets, and use the inter-annotator agreement to validate the reliability of the data.
- Over-batch the data. Smaller batches allow the model to learn from earlier rounds of data.
- Finally, monitor the compute costs. Caching the embeddings, reusing committees, and streamlining the selection process will maintain computational efficiency during the training phase.
Conclusion
Practically, the best approach to using active learning is likely to be a combination of multiple methods rather than simply one method. Hybrid query methods utilize a combination of uncertainty, diversity and rare class selection, and the proportion of each type of selection may vary depending on the improvement of the model’s metrics.
The most effective implementations of active learning view the process as a continuous feedback loop: test, measure and refine the process after every round of labeling.
Name, Job Title and Company Name:
Snehal Joshi, Director at HabileData
Author Bio:
Snehal Joshi heads the business process management vertical at HabileData, the company offering quality data processing services to companies worldwide. He has successfully built, deployed and managed more than 40 data processing management, research and analysis and image intelligence solutions in the last 20 years. Snehal leverages innovation, smart tooling and digitalization across functions and domains to empower organizations to unlock the potential of their business data.