ChatGPT from Scratch: How to Train an Enterprise AI Assistant
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In today's fast-paced business environment, the demand for intelligent enterprise assistants that can help optimize workflow, handle customer queries, and even automate tasks is at an all-time high. But how do you go about creating a powerful and reliable conversational agent like ChatGPT? This tutorial aims to answer exactly that question. We invite developers, data scientists, and tech enthusiasts to a rapid tutorial on building Large Language Models (LLMs) tailored for enterprise applications. We will delve into the architecture, training methodologies, data pipeline construction, and optimization techniques required for creating a state-of-the-art enterprise assistant. Participants will gain experience with LLM with a comprehensive LLM walkthrough along with the foundational knowledge required to build, fine-tune, and deploy LLMs in an enterprise setting. Key Takeaways: * A Brief History of LLMs: Tracing the evolutionary journey of Large Language Models from their rudimentary forms to cutting-edge architectures like GPT-4, and understanding their impact on the field of Natural Language Processing (NLP). * Understanding the core architecture and components of Large Language Models like ChatGPT. * Techniques for curating and pre-processing domain-specific datasets that result in a highly specialized and efficient LLM. * Strategies for efficient and cost-effective model training, from fine-tuning pre-trained models to training from scratch. * Deployment considerations for LLMs, including the use of cloud-based services like AWS and Azure. * Security and ethical considerations in deploying LLMs in a business environment, including data privacy and model interpretability. By the end of the tutorial, attendees will have a working knowledge of LLMs and the confidence to prototype intelligent conversational agents for their organizations. Whether you are a novice exploring the world of NLP and machine learning or an experienced developer looking to upskill, this tutorial has something for everyone. Come join us as we demystify the intricacies of developing enterprise-grade LLMs!
Transcript
In today's fast-paced business environment, the demand for intelligent enterprise assistants that can help optimize workflow, handle customer queries, and even automate tasks is at an all-time high. But how do you go about creating a powerful and reliable conversational agent like ChatGPT? This tutorial aims to answer exactly that question.
We invite developers, data scientists, and tech enthusiasts to a rapid tutorial on building Large Language Models (LLMs) tailored for enterprise applications. We will delve into the architecture, training methodologies, data pipeline construction, and optimization techniques required for creating a state-of-the-art enterprise assistant. Participants will gain experience with LLM with a comprehensive LLM walkthrough along with the foundational knowledge required to build, fine-tune, and deploy LLMs in an enterprise setting.
Key Takeaways:
- A Brief History of LLMs: Tracing the evolutionary journey of Large Language Models from their rudimentary forms to cutting-edge architectures like GPT-4, and understanding their impact on the field of Natural Language Processing (NLP).
- Understanding the core architecture and components of Large Language Models like ChatGPT.
- Techniques for curating and pre-processing domain-specific datasets that result in a highly specialized and efficient LLM.
- Strategies for efficient and cost-effective model training, from fine-tuning pre-trained models to training from scratch.
- Deployment considerations for LLMs, including the use of cloud-based services like AWS and Azure.
- Security and ethical considerations in deploying LLMs in a business environment, including data privacy and model interpretability.
By the end of the tutorial, attendees will have a working knowledge of LLMs and the confidence to prototype intelligent conversational agents for their organizations. Whether you are a novice exploring the world of NLP and machine learning or an experienced developer looking to upskill, this tutorial has something for everyone. Come join us as we demystify the intricacies of developing enterprise-grade LLMs!
About the speakers
CEO of Winder.AI, author of "Reinforcement Learning"