Editor's Note: The following is an article written for and published in DZone's 2024 Trend Report, Data Engineering: Enriching Data Pipelines, Expanding AI, and Expediting Analytics.
Remarkable advances in deep learning, combined with the exponential increase in computing power and the explosion of available data, have catalyzed the emergence of generative artificial intelligence (GenAI). Consequently, huge milestones have propelled this technology to greater potential, such as the introduction of the Transformer architecture in 2017 and the launch of GPT-2 in 2019. The arrival of GPT-3 in 2020 then demonstrated astounding capabilities in text generation, translation, and question answering, marking a decisive turning point in the field of AI.
In 2024, organizations are devoting more resources to their AI strategy, seeking not only to optimize their decision-making processes, but also to generate new products and services while saving precious time to create more value. In this article, we plan to assess strategic practices for building a foundation of data intelligence systems. The emphasis will center around transparency, governance, and the ethical and responsible exploitation of cutting-edge technologies, particularly GenAI.
Identifying and extracting data are fundamental steps for training AI systems. As data is the primary resource for these systems, it makes it a priority to identify the best sources and use effective extraction methods and tools. Here are some common sources:
Other important sources include NoSQL databases, IoT devices, social media, and APIs, which broaden the spectrum of resources available to AI systems.
Data quality is indispensable for training accurate AI models. Poor data quality can distort the learning process and lead to biased or unreliable results. Data validation is, therefore, a crucial step, ensuring that input data meets quality standards such as completeness, consistency, and accuracy. Similarly, data versioning enables engineers to understand the impact of data changes on the performance of AI models. This practice facilitates the reproducibility of experiments and helps to identify sources of improvement or degradation in model performance.
Finally, data tracking ensures visibility of the flow of data through the various processing stages. This traceability lets us understand where data comes from, how it is transformed, and how it is used, thereby contributing to transparency and regulatory compliance.
Advanced data transformation techniques prepare raw data for AI models. These techniques include:
These techniques are proving important because of their ability to enhance data quality, manage missing values effectively, and improve predictive accuracy in AI models. Imputation methods, such as those found in libraries like Fancyimpute and MissForest, can fill in missing data with statistically derived values. This is particularly useful in areas where outcomes are often predicted on the basis of historical and incomplete data.
Data management practices are evolving under the influence of AI and the increasing integration of open-source technologies within companies. GenAI is now playing a central role in the way companies are reconsidering their data and applications, profoundly transforming traditional approaches.
Let's take a look at the most critical considerations for building AI-driven data systems.
The use of open-source databases for AI-driven data engineering has become a common practice in modern data ecosystems. In particular, vector databases are increasingly used in large language model (LLM) optimization. The synergy between vector databases and LLMs makes it possible to create powerful and efficient AI systems.
In Table 1, we explore common open-source databases for AI-driven data engineering so that you can better leverage your own data when building intelligent systems:
New database technologies, such as distributed, unified, and multi-model databases, offer developers greater flexibility in managing complex datasets. Data-intensive AI applications need these capabilities to bring greater flexibility in data management. Additionally, privacy-oriented databases enable computations on encrypted data. This enhances security and compliance with regulations such as GDPR. These advances enable developers to build more scalable and secure AI solutions. Industries handling sensitive data need these capabilities to ensure flexibility, security, and regulatory compliance.
As shown in Table 1, homomorphic encryption and differential privacy solutions will prove impactful for advanced applications, particularly in industries that deal with sensitive data. For example, homomorphic encryption lets developers operate computations on encrypted data without ever decrypting it.
Ethical considerations related to training models on large datasets raise important questions about bias, fairness, and transparency of algorithms and applications that use them. Therefore, in order to create AI systems that are more transparent, explainable AI is becoming a major requirement for businesses because the complexity of LLM models often makes it difficult, sometimes even impossible, to understand the decisions or recommendations produced by these systems.
For developers, the consequence is that they not only have to work on performance, but also ensure that their models can be interpreted and validated by non-technical stakeholders, which requires extra time and effort when designing models. For example, developers need to install built-in transparency mechanisms, such as attention maps or interpretable results, so that decisions can be traced back to the specific data.
Building a scalable AI infrastructure is based on three main components:
Hybrid cloud solutions offer flexibility, resilience, and redundancy by combining public cloud resources with on-premises infrastructure. They enable the public cloud to be used for one-off requirements such as massive data processing or complex model training. At the same time, they combine the ability to maintain sensitive data on local servers. This approach offers a good balance between performance, security, and costs because hybrid cloud solutions enable organizations to make the most of both environments.
To ensure the future proofing of AI systems, it is essential to:
Collaboration between data engineers, AI/ML engineers, and data scientists is critical to the success of AI projects. Data engineers manage the infrastructure and pipelines that allow data scientists and AI/ML engineers to focus on developing and refining models, while AI/ML engineers operationalize these models to deliver business value.
To promote effective collaboration, organizations need to implement several key strategies:
These practices help create a cohesive work environment where information flows freely, leading to more efficient and successful AI projects. For example, in a recommendation engine used by an e-commerce platform, data engineers collect and process large volumes of customer data. This includes historical browsing data and purchasing behavior. AI/ML engineers then develop algorithms that predict product preferences, and developers integrate the algorithms into the website or application. When an update to the recommendation model is ready, MLOps pipelines then automate testing and deployment.
Beyond tool implementation, strategic considerations must be accounted for in the same way as purely technical ones:
As organizations move forward, they need to focus on data engineering automation, cross-functional collaboration, and alignment with ethical and regulatory standards in order to maximize the value of their AI investments.