Gaurav Vij

Most advanced and cost efficient methods for fine-tuning and deployment of custom AI models

Submitted Jun 3, 2024


With the evolution of Large language models (LLMs), the fine-tuning and deployment techniques have also evolved but many times developers are not sure which technique or framework would work best with their use-case.
In this session we will go through the different ways of tuning LLMs with their integration complexity, performance benefits, associated costs and challenges, and introduce MonsterAPI’s vertically stacked no-code LoRA/QLoRA powered LLM finetuning and deployment solutions designed to simplify the process and optimizing the GPU processing cost.

To further democratize the access of LLM finetuning and deployment capabilities, we introduce MonsterGPT - a state of the art chat powered AI assistant for LLM finetuning and deployments.

With MonsterGPT, developers can now optimize LLMs efficiently for domain-specific tasks using natural language commands. Our platform offers a LLMOps API while leveraging our scalable and affordable GPU cloud, drastically reducing costs and deployment times.

Intended Audience:

This session is ideal for AI/ML developers, data scientists, and product managers interested in enhancing model performance without the complexities of traditional infrastructural setups. It will also benefit business leaders looking to implement AI solutions swiftly and cost-effectively.

Problems Addressed:

Many organizations face barriers in deploying LLMs due to the need for specialized knowledge in fine-tuning, high infrastructure costs, and lengthy deployment times. Our solution addresses these challenges by providing a user-friendly platform that simplifies and accelerates the deployment process.

Scope of the Talk:

  1. Overview of LLM challenges in production pipelines.
  2. Introduction to MonsterAPI and its integration with LoRA/QLoRA technologies.
  3. Demonstration of ‘MonsterGPT’ and its capabilities in natural language-driven model fine-tuning.
  4. Case studies showing the effectiveness of our solution in reducing costs and time-to-deployment.

Attendees will gain:

  • A clear understanding of how to streamline LLM deployments using no-code solutions.
  • Practical knowledge on reducing costs and deployment times for AI projects.
  • Strategies for implementing scalable AI solutions without in-depth technical expertise in infrastructure management.
  • Insights into future trends and advancements in LLM fine-tuning technologies.

Format of the Session:

Interactive Demonstration with Q&A


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