The release of ChatGPT by OpenAI late last year has taken the world by storm. Enterprises around the world have seen the tremendous potential of Large Language Models (LLMs) and Artificial Intelligence (AI), with their transformative forces touching virtually every industry. Emerging uses range from content generation, virtual assistants, code generation, and debugging to education and training. However, there remain challenges like bias, problems with hallucinations, lack of transparency, accountability issues and privacy concerns.
Diving into the current landscape, we see two main streams of LLM development. On one side, we have closed or proprietary LLMs, with companies such OpenAI, Google, and Anthropic leading the charge–providing both end-user applications and Application Programming Interfaces (APIs). These models are state-of-the-art but come with some constraints for enterprise use cases, such as the requirement to send data externally and limited customizability. On the other hand, open LLMs are growing rapidly. Here, “open” encompasses both open-source models and those available for commercial usage. Models derived from LLAMA (by Meta) are notable contenders in this space. Enterprise consumers can customize these open models and run them locally within their data centers and clouds, providing greater security, and flexibility. On the margin, Enterprises tend to favor a future that champions safe, commercially viable, and transparent LLM development.
Between Possibility and Uncertainty
Amidst this dynamic landscape, enterprises globally face a conundrum. The allure of harnessing LLMs is strong, but the future remains shrouded in uncertainty, and it seems prudent to take actions that retain some flexibility and nimbleness.
At Beacon, we are excited about these developments. We foresee advancements in both closed and open LLMs, each complementing the other. As AI/LLM related technology matures and the knowledge around developing custom models becomes more widespread, an enterprise’s true competitive edge will lie in harnessing its distinct data and expertise, seamlessly embedding LLMs into its core business processes and applications. Below is an architecture that a typical enterprise may have to manage data and make use of LLMs to enhance the business processes and increase the productivity of end users.
Navigating the AI Revolution with Beacon Cloud
We’ve always emphasized the significance of platform-centric solutions. At its core, our cloud infrastructure fortifies the platform, ensuring top-tier security, unparalleled scalability, rapid speed, and seamless connectivity. This platform serves as the bedrock for all application development, offering a consistent developer experience that aligns perfectly with runtime. An enterprise’s entire data repository stays securely within its secure and segregated domain. Both applications and batch jobs benefit from the platform’s containerization, efficiently executed using the Batch Job and Grid Compute Schedulers.
Here is a look into how the components and packages available as part of Beacon’s Cloud Infrastructure provide a future-proof platform for harnessing the power and potential of LLMs.
For example, in Beacon Cloud, you can set up a cluster of GPU-enabled compute grids with the click of a button. You can start the Beacon IDE or Beacon Notebook to experiment with prompt engineering on top of proprietary APIs, or fine-tune open models. You can use Beacon’s Python-based transparent source SDK to orchestrate data loading, leverage internal and external APIs, and write production-ready code. Once you have a model that meets the objectives, you can maintain version control with the App/Model registry available inside your Beacon cloud.
Beacon’s batch scheduler is an ideal way for implementing a data pipeline and LLM/ML Ops pipeline where you can schedule batch jobs, long running server jobs with arbitrary constraints and support full monitoring/logging and alerting. Beacon’s app development framework makes it easy to write apps in Python, which is also the language of choice in the data science community.
All of the components used for harnessing LLM and ML are the same ones used for any other development. We’ve made a Beacon App Store module called “Beacon Machine Learning” that you can install, with a guide and easy-to-follow examples to help you get started on your journey into the world of LLMs, and we will be hosting a series of webinars on these topics over the next few months.