Building a Modern Data Infrastructure for AI and ML Workloads: Discriminative and Generative AI Models
Exploring the Intersection of Discriminative and Generative AI in Enterprise Data Infrastructure
In the realm of enterprise artificial intelligence, the distinction between discriminative and generative models plays a crucial role in shaping the data infrastructure of organizations. While generative AI has been in the limelight recently, the importance of discriminative AI cannot be overlooked. Both types of AI models serve distinct purposes, with discriminative models focusing on classification and prediction, and generative models on creating new data.
Organizations are recognizing the need to incorporate both discriminative and generative AI into their data infrastructure to operate efficiently and explore new revenue streams. Building a comprehensive data infrastructure that supports data analytics, data science, discriminative AI, and generative AI is essential for organizations looking to leverage the full potential of AI technologies.
The foundation of this modern data infrastructure lies in the concept of a modern data lake, which combines elements of a data warehouse and a data lake while utilizing object storage for all data storage needs. Object storage, designed for unstructured data, provides the scalability and performance required for storing vast amounts of data in a data lake.
Key to this modern data lake architecture are open table format specifications such as Apache Iceberg, Apache Hudi, and Delta Lake, which enable the integration of object storage within a data warehouse. These specifications offer advanced features like partition evolution, schema evolution, and zero-copy branching, enhancing the capabilities of traditional data warehouses.
By utilizing object storage as the foundation for both structured and unstructured data, organizations can create a unified data repository that caters to the diverse needs of AI and machine learning workloads. This modern data lake serves as the hub for collecting, storing, processing, and transforming data for various AI applications.
When it comes to discriminative AI, organizations must consider the storage requirements for both unstructured and semi-structured data used in training models. High-speed network and disk drives are essential for handling large training sets that cannot fit into memory, ensuring optimal performance during model training.
For generative AI, the focus shifts towards building a custom corpus with a vector database that indexes and stores documents alongside their numerical representations. This custom corpus serves as a repository of proprietary knowledge unique to the organization, enabling semantic search and retrieval-augmented generation techniques.
Fine-tuning large language models with information from the custom corpus can enhance the domain-specificity of AI models, albeit with considerations for compute resources and data dilution. Alternatively, retrieval-augmented generation offers a real-time approach to leveraging the custom corpus for generating contextually relevant responses without the need for model training.
In conclusion, the convergence of discriminative and generative AI within a modern data infrastructure presents organizations with a wealth of opportunities to harness the power of AI and machine learning. By adopting a comprehensive approach to data storage, organizations can unlock the full potential of AI technologies and drive innovation in their respective industries.
