Getting started with AI trust and safety

In the rapidly advancing field of artificial intelligence, data cleaning has become a mission-critical step in ensuring the success of Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) architectures. This blog emphasizes the importance of high-quality, structured data in preventing AI model hallucinations, reducing algorithmic bias, enhancing embedding quality, and improving information retrieval accuracy. It covers essential AI data preprocessing techniques like deduplication, PII redaction, noise filtering, and text normalization, while spotlighting top tools such as IBM Data Prep Kit, AI Fairness 360, and OpenRefine. With real-world applications ranging from LLM fine-tuning to graph-based knowledge systems, the post offers a practical guide for data scientists and AI engineers looking to optimize performance, ensure ethical compliance, and build scalable, trustworthy AI systems.

The Data Prep Kit (DPK) framework enables scalable data transformation using Python, Ray, and Spark, while supporting various data sources such as local disk, S3, and Hugging Face datasets. It defines abstract base classes for transformations, allowing developers to implement custom data and folder transforms that operate seamlessly across different runtimes. DPK also introduces a data abstraction layer to streamline data access and facilitate checkpointing. To support large-scale processing, it provides three runtimes: Python for small datasets, Ray for distributed execution across clusters, and Spark for highly scalable processing using Resilient Distributed Datasets (RDDs). Additionally, DPK integrates with Kubeflow Pipelines (KFP) for automating transformations within Kubernetes environments. The framework includes transform utilities, testing support, and simplified APIs for invoking transforms efficiently. By abstracting complexity, DPK simplifies development, deployment, and execution of data processing pipelines in both local and distributed environments.

The blog post explores how Kubeflow Pipelines (KFP) automate Data Prep Kit (DPK) transforms on Kubernetes, simplifying execution, scaling, and scheduling. It details the required Kubernetes infrastructure, reusable KFP components, and a pipeline generator for automating workflows. By integrating KFP, DPK streamlines orchestrating and managing complex data transformations.