This article recaps a session by Victoria Austin and Penn Sefton delivered during 3Cloud’s Envision Summit.
Explore the world of language models within the Lakehouse environment. Let’s uncover the tangible potential of LLMs utilizing Azure Databricks and Azure OpenAI alongside open-source LLM frameworks. Our goal is to simplify the complexities of LLMs and guide those interested in LLM adoption on their practical applications. We’ll showcase and demo how to tackle common challenges with LLMs and help you choose the right LLM implementation for your requirements.
The Lakehouse Paradigm
Data Lakehouse integrates two conventional data platforms: Data Warehouse and Data Lake.
Data Warehouse, a long-standing and reliable system for storing, securing, and governing structured data within the familiar SQL ecosystem. Ideal for business intelligence teams, it supports interactions with data through a language they are accustomed to. Despite its strengths, Data Warehouses fall short in handling unstructured text data, a limitation particularly pertinent to large language model use cases.
To overcome this limitation, the Data Lake emerged, offering enhanced adaptability and flexibility. Built on cost-effective object storage solutions, it accommodates unstructured, semi-structured, and structured data. However, as Data Lakes grow, they often transform into data swamps, becoming unwieldy and challenging to govern due to data replication, duplication, and siloing. Notably, a common approach involves copying data from the Data Lake to warehouses for business intelligence applications, introducing complexities.
This is where Data Lakehouse steps in, combining the strengths of both Data Warehouses and Data Lakes while mitigating their weaknesses. It provides a unified platform for analytics teams, supporting both BI and SQL work, as well as advanced AI and machine learning applications. Maintaining reliability and governance akin to warehouses, it leverages low-cost storage within the Data Lake and stores diverse data types in accessible formats.
Moving beyond the Lakehouse, let’s consider its competitive advantage in traditional analytics domains. By offering a foundation of quality, reliable, and understandable data, it enables BI and data science teams to work simultaneously on one platform. This architecture becomes particularly advantageous for AI and machine learning in the Azure environment.
An additional benefit of the Lakehouse platform, especially evident in DataBricks, is its understanding of data lineage. This capability facilitates easy tracing of source data, crucial for AI applications, and builds trust with both technical and non-technical users. Understanding data lineage addresses common questions about the reliability of source data.
On the developer side, Lakehouse, especially within DataBricks, provides a rich array of tools. The increased interest and support, particularly following the launch of GhatGPT and the integration of large language models into public discourse, highlight the platform’s capacity to support the development of such models on custom datasets.
Proving the value of LLMs
One commonly utilized application for LLMs is chatbots. Additionally, we see applications such as sentiment analysis, text classification, and natural language querying. The challenge is in showing how to implement these on your platform. Fortunately, conducting these steps is streamlined within the Lakehouse environment.
- Select a use case. Pick from a wide range of LLM use cases and align these to your business model.
- Map out the design steps for your use case. Determine all necessary steps to design the solution end-to-end.
- Develop a proof of concept. Show that the build is possible through a minimum viable product. The primary goal is to ensure feasibility and demonstrate that the envisioned modifications can be implemented.
Learn more about Simplifying Complexities of LLMs in our checklist.
Technical Demo
Access the video demonstration on 3Cloud’s YouTube. Learn how to pick your use case and see the advantages of starting simple, then adding complexity to your prompt. This also explores specialized LLMs and fine tuning an existing LLM.
Want To Learn More?
Hear more sessions from our Envision Summit. For more inforomation on tackling common challenges with LLMs or help choosing the right LLM implementation for your requirements, contact us to get started.
