AI is a technology that offers a plethora of opportunities but also its fair share of challenges. The success of AI-based projects depends largely on the quality of the data used for training AI models. We wanted to dedicate this article to the data-related processes that are integral to creating custom AI solutions. As a company that specializes in developing such systems, we know how crucial the quality of collected data is. We also know how to use it the right way to achieve the business objectives of a specific company.
In this article, we showcase the best practices for data collection and processing. It’s a guide for all brands that want to rely on AI to take their business to the next level.
The crucial role of high-quality data
Before we dive into particular strategies, it is essential to underline the significance of quality data. The saying that applies to AI development states: “Garbage in, garbage out.” If the information sets used for training an AI model are flawed, biased, or incomplete, the results produced by the AI system and its decisions will reflect these defects. Every brand that wants to seriously approach AI implementation should focus their efforts on data quality.
Here are our recommended guidelines to follow in order to streamline this stage of AI system production:
Define clear objectives
Every AI project should start with establishing well-thought, concise objectives. Understanding precisely what the AI model should be able to achieve and what type of data is required is the first step. Having a list of priorities and expected outcomes serves as the guiding light for data collection efforts.
Identify diverse data sources
Having multiple, versatile data sources provides a more comprehensive perspective. It all depends on an industry or a type of AI system. These sources may include structured databases, unstructured text, images, audio files, video clips, sensor data, and more. If you measure certain variables like behavior logs, transaction histories, or customer reviews, insights from them will be valuable, too.
Data privacy and compliance
Ensure your data collection and handling procedures align with relevant regulations and standards. Depending on your industry, this might include GDPR, HIPAA, or industry-specific guidelines. Upholding data privacy and compliance is not only a legal requirement. It’s also essential for maintaining the trust of your customers and the integrity of your project.
Strategies for effective data collection
- Data sampling: This method considers the representativeness of data. Sometimes collecting the entire dataset is impractical due to its size or diversity. Stratified sampling ensures that training data accurately reflects the broader dataset’s characteristics.
- Active learning: In cases where labeled data is scarce or costly to obtain, guiding the model to select and mark certain data points proves to be an effective technique. The algorithm can focus on the most informative examples, reducing the overall labeling burden.
- Crowdsourcing: Such platforms come in handy when tasks require human annotation or categorization. They enable brands to access a large pool of human annotators who can generate labeled data quickly and at scale. Crowdsourcing is useful for tasks like sentiment analysis and content categorization.
- Data augmentation: This methodology involves applying various changes to the existing data to generate new training examples. For example, in computer vision, it can include rotation, scaling, and flipping. Data augmentation can significantly increase the diversity training data without the need for additional manual labeling.
- APIs and web scraping: Tasks that require specific types of data will benefit from these two techniques. Many online spaces provide API access that enables programmers to access their data. Web scraping means extracting information from websites directly. The crucial point here is to consider legal and ethical regulations by respecting the terms of service agreements.
- Data partnerships: In some cases, businesses can benefit from collaborations with other organizations that already possess valuable datasets. This can be particularly advantageous when trying to access specialized or domain-specific data that may not be readily available through traditional sources.
- Sensor networks and IoT devices: Thanks to IoT and sensors, data can be collected directly from connected devices. They can provide real-time, high-frequency data for various applications like forecasting certain events or monitoring specific spaces.
How to process collected data?
Processing is the next critical step that comes after data collection. Raw data is rarely suitable for direct use in AI models. This stage includes adjusting datasets to enhance their quality, relevance, and compatibility with chosen algorithms. Here’s how to perform it:
Cleaning and handling missing data
The initial phase of data processing involves locating and identifying missing values, outliers, and anomalies within the dataset. Techniques such as mean, median, or mode imputation prove to be invaluable for filling gaps in the data. In cases where relationships within the dataset are more complex, regression imputation is employed. Ultimately, outliers that negatively influence the dataset can be removed.
Feature engineering
Following the cleanup process, a thorough examination of the dataset takes place. Its goal is to pinpoint the most important features of a specific AI model. Variables undergo transformation as necessary, including the application of logarithmic or exponential transformations. If the creation of new features promises to provide additional insights, it is strongly encouraged. Moreover, the integration of dimensionality reduction techniques, such as Principal Component Analysis, may also be applied.
This process also takes place later to improve the performance of AI models. If the system is highly biased or underfitting, creating new variables by combining others is the best approach to achieve a better level of complexity, which allows for advanced feature development. On the other hand, when overfitting occurs, it’s a good practice to add a larger data set, so the model has more opportunities to discover relationships between variables and can return proper results for non-training data.
Addressing class imbalance
Next, the data processing team analyzes the class distributions to identify any imbalances, particularly in classification tasks. Several approaches are employed to address them:
- Applying oversampling techniques to increase instances in the minority class.
- Implementing undersampling techniques to reduce instances in the majority class.
- Evaluating the efficacy of specialized algorithms like the Synthetic Minority Over-sampling Technique (SMOTE).
Data normalization or standardization
This step guarantees that no single feature overwhelms the learning process. It enhances the AI model’s ability to learn from the data. Moreover, it promotes stability and efficiency in the training process. Two widely employed techniques for achieving this are:
- Min-max scaling (normalization): It involves rescaling features to a range between 0 and 1. By doing so, it ensures that all features share a comparable scale, preventing any one attribute from dominating the learning process. This method is particularly useful when features have varying units or scales.
- Z-score scaling (standardization): It removes the mean and scales the features to have a standard deviation of 1. This maintains the distribution of the data while ensuring that each feature contributes equally. Standardization is advantageous when features are normally distributed and exhibit different variances.
Handling categorical variables
Categorical variables, representing qualitative data, must be appropriately encoded for the model to effectively process them. This way, it gains the capacity to utilize vital information in its learning process, leading to accurate and reliable predictions. There are two main ways to approach this:
- One-hot encoding creates binary columns for each category, indicating its presence or absence. This method is particularly crucial for nominal variables, where there is no inherent ordinal relationship between categories. It ensures that the model does not misinterpret categorical variables as having an ordered relationship.
- Label encoding, on the other hand, assigns a unique numerical value to each category based on its order. This approach is suitable for ordinal variables, where there exists a meaningful order between categories. By preserving this ordinal information, the model can better understand the relationships between different categories.
Splitting data for training and testing
In this phase, one data portion is designated for training the model. The other is reserved for evaluating its performance. The training set provides the foundation for instructing the model, allowing it to learn patterns and relationships within the data. On the other hand, the testing set serves as an independent benchmark, allowing for an unbiased assessment of the model’s accuracy and generalization capabilities. Striking the right balance between these two segments is crucial for building a robust and reliable AI model.
Reviewing and iterating
After the preprocessing steps have been implemented, a thorough review of the preprocessed data is performed to check if it aligns with the requirements of the chosen AI algorithms. This quality assurance step is crucial to identify any potential anomalies that may impact the model’s performance. If further adjustments are necessary, the preprocessing steps are revisited and refined accordingly. The iterative approach ensures that the data is fine-tuned and optimized to maximize the model’s potential and accuracy in making predictions or classifications.
What’s next: Model training and evaluation
After data processing is done, AI model training begins. It is evaluated every step of the way to detect any malfunctions and ensure successful implementation. This phase involves selecting an appropriate algorithm, feeding it with prepared data, and iteratively refining the model’s parameters to adjust performance.
It’s essential to choose the right algorithm based on a specific use case. Each system will require different capabilities. Your AI implementation partner should be aware of the objectives beforehand to prepare a strategy for data collection and processing, as well as model training.
Through careful selection of algorithms and rigorous evaluation, the team responsible for your AI solution can deliver accurate and reliable results for your business. That’s why betting on a custom AI system is the best approach. It ensures that the final result meets the expectations of the company that will use it to boost their operations.
Conclusion
Overcoming data challenges in AI development is a critical step towards building effective and reliable AI models. By establishing clear objectives, leveraging diverse data sources, and implementing robust preprocessing techniques, businesses can ensure that their custom AI solutions deliver meaningful results.
The quality of data serves as the foundation of any AI project. Investing time and resources in collecting and preparing data properly is not just a best practice; it’s a prerequisite for AI success. High-quality data empowers AI models to make accurate predictions, automate tasks, and drive innovation across industries.
G-Group.dev follows modern industry standards. We design the process of AI development and implementation according to your needs. If you consider introducing AI within your organization’s structures – we are the trustworthy partner you can collaborate with to get the desired outcome. Book a free consultation call and let’s discuss how we can use AI to improve your business in almost any area you can imagine.
