Abstract: LLMs, such as GPT-4 , offer a novel alternative to ETL by generating complex code from simple text prompts, enabling non-technical users to build AI data pipelines independently. The traditional approach to designing ETL workflows often requires knowledge of programming languages, which can be challenging and discouraging for non-engineers. Although no-code ETL tools provide a quick and easy solution, they often lack flexibility. MLtwist's vision aligns with this concept, creating a GPT-4-powered ETL platform that empowers users to construct intricate workflows without developer intervention (referred to as """"Phase 1"""").
ChatGPT also empowers chat-like experiences, resembling notebook-driven development, which significantly benefits ETL development. Traditionally, non-engineers provide requirements to an engineer. However, with LLM-based ETL tools, technical non engineering users can specify requirements step-by-step in a conversational manner, testing components directly in the platform, and progressing iteratively. MLtwist envisions this as the future of ETL development, referred to as """"Phase 2,"""" combining a component library and a chat interface to create custom workflows iteratively.
Furthermore, GPT-4 exhibits semantic understanding capabilities, enabling augmentation of prompts with knowledge of existing workflows and components. Instead of generating code, MLtwist's prompt engine will query a vector database to find preferred components or similar workflows for reuse. Additionally, GPT-4 can understand metadata and enforce data policies, aiding in identifying sensitive data, ensuring proper parsing and formatting, and aligning with customer needs. Integrating enterprise workflows and metadata with GPT-4 and vector databases, results in the culmination of a natural language interface, iterative environment, and semantic understanding represents """"Phase 3"""" for MLtwist. In this phase, non-technical users can describe a desired outcome, and the platform will generate or find the appropriate components, add necessary metadata, and provide valuable suggestions. This vision is supported by tools like LangChain and AutoGPT, demonstrating its feasibility.
Bio: Before founding MLtwist in 2021, David Smith held leadership roles at Google and Oracle. He is an expert in getting complex sensitive unstructured data ready for AI, and has launched first of kind complex ML/AI data partnerships with Oracle, Google, JD Power, and dozens of others. David holds a Bachelor of Science in Computer Science and Engineering from UC Davis and completed Google's Business Academy with Duke Corporate Education.
