AI Data Preparation: A Data Engineer’s Guide to Quality Inputs & Optimal Inference

What if the secret to a smarter, more sophisticated AI wasn’t the model, but the data you feed in?

As a data engineer, you are the architect working behind the scenes in AI data preparation. From curating and cleansing to structuring data, you hold the key to unlocking the full potential of AI inference. And it doesn’t stop there. You finetune data pipelines and do frequent quality checks. On the whole, it’s all about turning raw datasets into intelligence-ready inputs. The richer your data is, the stronger your AI decisions and outputs are. And this blog will uncover everything you need to know to turn preparation workflows into smarter AI outcomes. 

What Is AI Data Preparation? Defining the Process & Key Steps

AI data preparation is the stage in which you gather, purify, sort and change unrefined data into a format ready for training and using AI and ML models. This is a procedure that requires very accurate, precise, and relevant data because all of this directly affects the model's performance and trustworthiness.

With quality data comes better model outputs. And when about 78% of companies today are using AI in at least one business function, high-quality data serves as a critical cornerstone to its growing adoption. (Source) Here are the key steps to follow to achieve quality data and ideal model outcomes:

• Data collection - The first step involves data collection, where data that is varied, relevant, and unbiased is collected from multiple sources such as APIs, logs, and third-party providers. 

• Data cleansing - At this point, you deal with the missing parts or the inconsistent values, get rid of duplicates, correct errors, and outliers. In short, you clean data sets for more reliability. 
• Data integration - Here, you combine all data acquired into a unified dataset, resolving format differences and any other inconsistencies. This step enhances accessibility and reduces silos, allowing AI models to work with rich data. 
• Data transformation - At this juncture, you perform tasks like normalizing numerical ranges, generating feature representations, and encoding categorical variables. In short, it’s about converting data into suitable formats for efficient model training. 
• Data labeling - Your task is to apply relevant labels to data to make sure that the model recognizes the appropriate patterns and outputs accurately.
• Pipeline management - In the end, through the utilization of data pipelines, the automation of data preparation workflows is achieved. This is a refinement process that aims primarily at minimizing human errors and ensuring the data is current.

Primary Purpose of Data Preparation in AI

Let’s look at some of the key reasons why AI data preparation is necessary:

Accuracy - The quality of AI model outputs is dependent on the use of exact and non-biased data. In contrast, the outcomes will be distorted. Data processing takes the necessary precautions, thus ensuring that inputs are accurate and free from errors.

Consistency - AI Data preparation standardizes data formats and values across datasets, maintaining a reliable basis for analysis by AI systems. This way, the models can process information efficiently without misinterpretations. Therefore, consistency is key to aligning data with expected structures. 

Model readiness - The availability of model training and inference is through the process of cleaning, normalizing, and formatting datasets that come from the initial raw and unstructured data. It usually involves tasks like feature scaling and treating missing values that make datasets suitable for training and inference.

Data Collection & Ingestion

Data collection along with ingestion in AI data preparation, are the foundational stages that enable the creation of effective data pipelines. It is vital for a data engineer to have a good grasp of the associated concepts:

Source discovery

This process identifies and registers all possible origins of required data. Some of these origins can be relational databases, cloud data warehouses, SaaS platforms, IoT devices, or messaging platforms. As a data engineer, you can always map your data landscape and list each source, then rate its update frequency to see which needs periodic sync. 

API connectors

How would you handle a missing field or varying schema in the AI data preparation pipeline? With API connectors, you bridge the gap between your data and external systems. Prebuilt connectors like REST or GraphQL APIs assist with that through the extraction and importing of structured data. Plenty of ingestion tools come with ready-made connectors, and if they don’t meet specific needs, custom code is written for data formats and authentication.

Streaming vs Batch Ingestion

Data ingestion in AI data preparation can be done in two different modes:

Normalization & Transformation

Normalization and transformation are two essential steps in AI data preparation that boost model accuracy and training efficiency. If you’re a data engineer building preprocessing pipelines, the following steps ensure data is in a more suitable form for models:

Scaling

This step simply involves adjusting numerical data to a uniform scale to help ML algorithms perform better. There are two techniques useful for improved model convergence:

• Min-max normalization - rescales feature values to a fixed range when data is uniformly distributed.
• Z-score standardization - transforms features to have a mean of 0 and a standard deviation of 1. This is referred to when data follows a normal distribution and isn’t sensitive to outliers. 

Encoding categorical variables

In AI data preparation, this step converts non-numeric category data into a numerical format that algorithms can process. Encoding also has some widely used methods:

• Label encoding - Assigns a unique integer to each category and is useful for datasets where categories have a natural order. 
• One-hot encoding - Creates binary columns for each category and is preferred for nominal data without an intrinsic order.

Feature generation

New informative features are produced from raw data which improves the learning process of the model. Some instances of this are:

• Extraction of date-time parts
• Use of domain knowledge for more aggregated features
• Merging or changing of variables

Data Enrichment & Augmentation

Data enrichment is where you add value to an existing dataset by incorporating additional information from external sources. Data augmentation, on the other hand, focuses on creating modified or synthetic versions of existing data to artificially expand the training set. That said, let’s look at some of the data enrichment and augmentation techniques in AI data preparation:

External data sources

Incorporating external data sources means including demographic details, behavioral data, or technographic information relevant to the existing dataset. For example, you could enrich customer data with socioeconomic status or browsing behaviors. 

Synthetic samples

In AI data preparation, these are artificially generated samples that mimic the statistical properties of real data without deliberately copying existing records. As a data engineer, this is beneficial for overcoming data scarcities or mitigating privacy issues. Taking the example of medical imaging, generating synthetic tumor images helps train detection models more efficiently without the need to keep feeding in new data. 

Generative techniques

GANs (Generative Adversarial Networks), adversarial training and neural style transfer belong to the category of sophisticated generative techniques that have been implemented in this domain. Application of these approaches would lead to the generation of hyper-realistic variations or even fresh data points in the form of images, audio, and text.

Feature Engineering for AI/ML

Feature engineering is a very significant process in AI data preparation as it includes the selection, creation, and transformation of input variables to make them more able to predict through ML models. There are some key concepts of feature engineering as follows:

Domain-specific features

The features mentioned are derived from the expert knowledge of selected domains/industries. They make the most of domain insights or business rules that uncover hidden patterns or relevant data attributes that automated processes usually miss. One example in the case of commerce could be customer lifetime value. Another example, in the case of finance, could be a debt-to-equity ratio. The emphasis on real-world relevance leads to an increase in accuracy and interpretability.

Interaction terms

Interaction terms, during the process of AI data preparation, signify associations between two or more variables which together may have a considerable effect, even if their individual effects are insignificant. Interaction terms are built through applying mathematical operations on several features, for example, multiple features can be represented in the model by multiplication and addition, thus assisting the model in recognizing the complex patterns. A case in point is an interaction term in machine maintenance where temperature and machine usage hours might be multiplied together to yield a better prediction of machine failures.

Automated feature synthesis

This basically uses tools or advanced algorithms to systematically generate new features from raw and relational data. The principal technique to emphasize in this case is deep feature synthesis, which applies various mathematical operations on base features to create intricate feature combinations in a continuous manner. The scaling of this process through automation reveals even some very fine data patterns that may not be identified by the domain specialists.

Data Labeling & Annotation: Best Practices for Supervised Learning & Quality Control Protocols

Data labeling and annotation have fundamental importance in the AI data preparation process. They are critical for supervised learning, where models learn effectively through labeled datasets. That said, here are some best labeling practices that you can follow as a data engineer for quality control:

• Define clear and detailed labeling guidelines and quality standards to avoid variations or confusion among annotators.
• Periodic reviews, cross-checks, and audits should be carried out to ensure the accuracy of datasets.
• Annotators should be given detailed training on labeling instructions.
• A human-in-the-loop system can be utilized, where human annotators confirm the labels produced by machines.
• It might be a good idea to create dashboards that display the status of annotation and the metrics of quality.

Data Partitioning & Sampling

In the course of AI data preparation, data partitioning and sampling are procedures that separate datasets into various parts allowing for model generalization and precise evaluation. Therefore, a data engineer whose goal is to create trustworthy AI models must be aware of the following methods:

Train/Validation/Test splits

Under this technique, the dataset is split into three different sections:

• Training set (60-80%) - Used to train the model.
• Validation set (10-20%) - Used to tune hyperparameters and prevent overfitting.
• Test set (10-20%) - Evaluates final model performance on unseen data.

Random sampling is often used to create these splits, and this separation ensures unbiased assessment. 

Stratification

In AI data preparation, stratification separates the datasets so that every small part has the same proportion of the categories as the original dataset. This is a very important step for unbalanced datasets in which some categories have only a little number of samples. A stratified split will for instance keep the 80:20 ratio in the training, validation, and test splits if 80% of the samples are "dogs" and 20% are "cats."

Time-series considerations 

Random splitting is typically not suited for time-series data because temporal sequence and causality must be preserved. This is where datasets are split based on time to simulate real-world forecasting. Data from earlier periods is used for training, the middle period is for validation, and the last one is for testing. This approach of partitioning not only prevents data leakage but also guarantees that the model is strong enough to make predictions.

Role of Data Preparation in AI Inference

AI data preparation plays a critical role in AI inference by ensuring data fed into the models is clean, consistent, and processed at low latency. Here’s an elaboration on why:

Runtime preprocessing

This indicates that the raw data is transformed into a suitable format at the time of inference requests made which is at the same time and with the same technology steps (cleaning, feature extraction, and encoding) as the model training. Consequently, the training and inference are consistent. When this procedure is executed correctly, the incoming data is still model-friendly.

Consistency checks

AI data preparation involves checking of data types very early on, which is one of the most important steps. They point out the irregularities like missing or out-of-range values, schema migrations, and incorrect inputs to the sematic before they reach the AI model. Proper consistency checks remove the risk of unpredicted errors and non-reliable forecasts being produced, therefore, strengthening the stability of AI systems.

Low-latency pipelines

In order to support real-time AI applications, there is a need for inference pipelines to operate with very little delay. Therefore, AI data preparation at runtime has to be managed as low-latency pipelines allowing quick data transformations with high quality still maintained. Speed and thorough preprocessing balance is very important as it is the only way to keep the fast throughput.

Automated vs. Manual Data Preparation Tools

Data preparation can be done either manually or via automation through AI. As a data engineer, taking the automated route is ideally the best option when compared to manual methods. Here’s why:

Manual approaches usually involve the use of spreadsheet apps like Microsoft Excel or Google Sheets. Automated data preparation platforms include:

• Paxata - Built for AI-powered enrichment, profiling, and quality checks with visual collaboration.
• Improvado - Equipped with pre-built connectors and transformation logic, this is purpose-built for marketing and analytics. 
• TIBCO Clarity - A robust data preparation tool primarily used for data profiling, cleansing, and preparation.

Tredence also provides a comprehensive suite of AI data preparation accelerators as part of its ATOM.AI ecosystem to expedite data modernization and AI implementation. They offer complete pipeline observability, also supporting predictive analysis and various GenAI use cases.

Data Lineage & Governance

In AI data preparation, data lineage refers to the systematic tracking of data from its origin to its final use. Governance, in this context, refers to policies that manage data availability, usability, integrity, and security throughout the entire data lifecycle. As a data engineer, the following methods can help you with tracking and governance:

Metadata tracking

This technique captures detailed information about data attributes, source systems, transformations, and usage within AI workflows. Data flows can be visualized and the proof of data and its inter-connection through AI models can be recognized. Such a tracking method also allows for greater visibility, which means debugging problems can be detected and the quality of data being used for model training can be established.

Audit trails

These are thorough documentation of data handling operations that provide a complete log of every modification, access, or processing step that the data goes through. As a data engineer, this is your most effective way to track down errors to their origins and do an impact assessment before making system changes. Finally, audit trails also enhance data stewardship by allowing proactive remediation of data quality issues. 

Compliance requirements 

As always, when AI tools are concerned, data governance and compliance are a few aspects that cannot be ignored. Your data practices for AI will have to be in line with laws like GDPR, HIPAA, etc. It will be harder for companies to hide wrongdoings with data because one of the benefits of data lineage is that it provides an irrefutable record of data's flow and transformation that can be used as evidence if any non-compliance inquiries arise. Besides, having a well-established trust in data and AI systems is really crucial to preventing financial and reputational risks.

Scalable Architectures

Scalability in AI data preparation focuses on handling data volumes, velocity, and variety with zero bottlenecks. Below are some of the factors that contribute to scalability in data architectures:

Edge-to-cloud workflows

Here, processing is done partly on edge devices and partly in cloud platforms. While edge reduces latency by taking care of real-time tasks, the cloud offers more flexibility and storage, making heavy AI data processing possible. In the end, data is filtered to the edge to save bandwidth before syncing it with the cloud. 

Distributed processing

Distributed processing is where data workloads are split across machines running in tandem to speed up large-scale data transformations. An AI data preparation platform manages these tasks with tolerance, ensuring quick, consistent data prep even if some nodes fail. Synchronization mechanisms ensure further consistency and integrity of results aggregated from multiple nodes. 

Containerized pipelines

Data prep steps are packaged into containers like Docker, which encapsulate code and dependencies for consistent deployment. Orchestration tools like Kubernetes handle independent scaling and maintenance of pipeline components. On the whole, containerized pipelines are responsible for modular and portable workflows across edge and cloud. 

Monitoring & Continuous Improvement

AI data preparation is not just a one-time event, as it involves various processes and avenues for refinement. And as a data engineer, there are ways around it to maintain reliable models in production:

Data drift detection

This refers to changes in the statistical properties of input data that can cause model degradation if not detected earlier. You can prevent this by:

• Continuously tracking metrics like schema changes and feature distributions.
• Implementing automated alerts based on deviations to flag potential drifts.
• Employ tools that visualize distribution changes and flag anomalies in real-time.

Retraining triggers

Drift detection in AI data preparation alone isn’t enough. You have to retrain models as well. Here’s how you can do that:

• Schedule regular retraining cycles (Can be either monthly or quarterly).
• Use drift detection as a trigger for off-cycle retraining.
• Version datasets to safely test new model versions and rollbacks. 

Feedback loops

Feedback loops facilitate continuous improvement that improves AI data preparation and model accuracy. And there are some good strategies for this, too:

• Collect performance data and user feedback to identify concept drift or quality issues. 
• Encourage cross-functional collaboration between data engineers and domain experts to refine features and data cleaning rules.
• Use monitoring dashboards that visualize model health in real-time for proactive detection and resolution. 

Wrapping Up

As always, when it comes to data handling, quality is superior to quantity. And it’s not just quality. It includes readiness and relevance, too. Your smart AI data preparation strategies are the silent engines behind robust inference and scalable model outcomes. And at Tredence, we enhance your data prep goals with the right solutions and expertise needed.

As your AI consulting partner, we help you maximize data quality, manage master data, and modernize data so you can build high-scale AI applications that make an impact. And our range of accelerators also expedite data preparation and transformation, allowing you to manage the end-to-end data lifecycle, from ingestion to consumption. 

Get in touch with us and take the next leap in AI data preparation!

FAQ

1] How do you perform effective feature engineering for AI and machine learning models?

To begin with, the feature engineering that is done hand-in-hand with AI data preparation is to a great extent the result of strong domain knowledge, the iterative process of refinement and selection of features that add to the predictive power. The whole process is about properly and sometimes radically selecting, transforming and creating input variables such that they:

1. Uncover the right patterns
2. Enhance the performance of the model
3. Avoid overfitting
4. Make the convergence of training faster

2] What strategies exist for enriching and augmenting datasets for AI training?

Data enrichment and augmentation strategies include:

• Applying transformations like rotation, scaling, and noise injection.
• Synthetic sample generation for more data diversity.

Note that these strategies vary by data type and may often leverage GANs or meta-learning for realistic augmentation. 

3] How does data labeling and annotation affect model performance?

In AI data preparation, high-quality data labeling and annotation directly impact model performance by:

• Providing structured and context-rich inputs.
• Improving generalization.
• Coming up with more confident predictions.

Note that if labels are poor/inconsistent, model accuracy and reliability may be drastically reduced. 

4] How does synthetic data generation support AI model development?

For AI model development, synthetic data generation creates artificial datasets that supplement scarce real-world data. This facilitates model training while preserving privacy. It also improves the model’s resilience to processing diverse datasets and handling varied scenarios. 

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Editorial Team
Tredence