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Launching Towards AI’s Free “Train & Fine-Tune LLMs for Production” Course
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Launching Towards AI’s Free “Train & Fine-Tune LLMs for Production” Course

Last Updated on November 5, 2023 by Editorial Team

Author(s): Towards AI Editorial Team

Originally published on Towards AI.

Launching Towards AI’s Free “Train & Fine-Tune LLMs for Production” Course

Towards AI is thrilled to announce the launch of the free and comprehensive course Training and Fine-tuning Large Language Models (LLMs) for Production, the second installment of the Gen AI 360: Foundational Model Certification. This initiative was made possible thanks to the robust collaboration with Activeloop and the Intel Disruptor Initiative and is greatly supported by Lambda and Cohere.

The course builds upon the success of our previous installment, the LangChain and Vector Databases in Production course, with tens of thousands of course takers. It is designed as a mix of 9 high-level videos, 40+ self-paced text tutorials, and 10+ practical projects like training a model from scratch or fine-tuning models for financial or biomedical use cases. “We’ve designed the course to cut through the noise of the latest rapid advancements in the LLMs, distilling it into a strategic pathway to empower technical professionals and executives to navigate the world of LLMs with efficiency and production readiness in mind,” said Louie Peters, CEO and Co-Founder of Towards AI.

Bridging Theoretical Knowledge with Practical Expertise for Production-Ready LLMs

“Training and Fine-tuning LLMs for Production” is designed to provide participants with deep, practical insights into the world of LLMs. It takes you on an exploration through the intricacies of training, fine-tuning, and implementing large language models in real-world business applications, ensuring that the knowledge imparted is readily applicable in professional settings. Participants will work on reinforcement learning from human feedback (RLHF) to improve an LLM, fine-tune a model on a business use case such as extracting disease-chemical relations from papers, or train an LLM from scratch.

Targeting Python professionals, our modules guide learners through strategic compute utilization during model training or fine-tuning. They empower them to make sound choices in resource allocation and technique selection, ensuring state-of-the-art, cost-effective, and efficient model development.

“LLMs offer tremendous potential. However, understanding their economic implications is crucial for enterprises considering their adoption. Companies need to understand the cost structure of training, fine-tuning, and productizing an LLM. This course represents a state-of-the-art blend of software like Deep Lake, LLM-optimized hardware, and groundbreaking Gen AI platforms that enable companies to train and fine-tune production-ready LLMs without breaking the bank”, said Davit Buniatyan, Activeloop CEO and Co-Founder.

Course Curriculum

  • Introduction to LLMs: Exploring foundational LLM concepts
  • LLM Architecture: Diving into model architectures
  • Training LLMs: Data management and ensuring quality in training data with Deep Lake and beyond. Strategies for effective model training and using Deep Lake for optimal data loading and compute utilization
  • Fine-tuning LLMs: Optimizing models for specific uses across business verticals (e.g. financial and biomedical)
  • Improving LLMs with RLHF: Applying reinforcement learning with human feedback to better LLM performance
  • Deploying LLMs: Strategies for real-world deployment with LLM-optimized compute
  • Advanced topics: Navigating through LLM ethics, scaling laws, model collapse, and future LLM training challenges

This course is a goldmine of knowledge for technical professionals, offering a deep dive into the intricacies of training and fine-tuning models, ensuring optimal resource utilization, and providing a hands-on experience through real-world projects and case studies.

Tech executives, on the other hand, will find value in watching the available 1.5hrs of video content, understanding the strategic and economic aspects of implementing LLMs, and ensuring that their teams are not merely utilizing resources effectively but are also making informed, strategic decisions that align with organizational goals and ethical considerations.

“I believe engineers and technology executives could greatly benefit from taking this course to stay at the forefront of AI,” said Arijit Bandyopadhyay, CTO — Enterprise Analytics & AI, Head of Strategy — Cloud & Enterprise, DCAI Group at Intel Corporation. “Intel continues to be at the vanguard of AI and new technology adoption. This Foundational Model Certification could help better equip the next generation of innovators with what they need to succeed with Generative AI and Large Language Models. It could also contribute to the broader adoption of AI applications and solutions across various industries.”

Free Compute Credits, Enabled with the Support of Course Partners Cohere and Lambda

With the generous support of our partners, the qualifying candidates who successfully pass the required chapters will unlock exclusive access to Lambda and Cohere credits, facilitating a smoother and more resource-optimized learning journey. This course is not just a certification; it is a safeguard against unnecessary computational expenditures, a guarantee to optimize resource utilization, and a promise to implement LLMs in a state-of-the-art and financially sound manner.

Seize the opportunity to be at the forefront of Generative AI and Large Language Models, and ensure your team navigates the complexities and potential of LLMs with strategic and economic proficiency. Enroll in our course for free now, and complete the required chapters to be among the qualifying participants to get the compute credits from our partners.

Learn more about the Gen AI 360: Foundational Model Certification here.

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