Scaling GenAI: Experimentation to Transformation
A Domino Virtual Event Series
Generative AI is transforming the way we do business. The technology is rapidly progressing from experimental novelty to a core business enabler. Domino's video channel, Scaling Gen AI, will help enterprise teams overcome the hype and take action. The channel offers two series focused on the needs of leaders and practitioners.
The Executive Insight series offers the strategic guidance you need. We will help you choose between traditional and generative models. You will learn what tools you need, the risks, and how to successfully navigate the path to ROI. Find when to go big with models and when small means better. Most importantly, we discuss and demonstrate how to scale this revolutionary technology.
The Builder's Toolkit series will give practitioners a clear overview of the latest techniques, models, and tools. Get started on Domino with prompt engineering, vector embeddings, fine-tuning while saving time and reducing costs. Deal with large models using clusters and multiple GPUs. See how to get models to production faster and responsibly and monitor model performance with the latest innovations.
There's a lot to learn, and it's all here. Don't wait, jump in! Sign up now!
Executive Insight Series
Strategic guidance for understanding and planning for this transformative technology
The Builder’s Toolkit Series
Practical implementation aspects, tools, and knowledge needed to make GenAI meet your use cases
Executive Insight Series
Building Genai with Global Impact
Lessons from the First GenAI Killer App
- Key capabilities for operationalizing GenAI models at scale
- Success factors for GenAI use cases
- Common challenges and how to avoid them
- Inferencing GenAI models cost-effectively
Anaconda CEO Peter Wang
Reflections on the Anniversary of ChatGPT
- The state of GenAI: where GenAI is delivering and missing expectations
- The challenges: the real risks and remaining barriers to impact
- The future: what advances are underway and what can we expect over the next year
episode 3
Shatter the Myths of Generative AI
We tackle the biggest misconceptions around driving business value with GenAI, including...
- Generative AI is the most important kind of AI
- Bigger is better
- You don’t need data scientists for Generative AI
- I need to hire prompt engineers
- LLMOps is a separate discipline from MLOps
- Someone else is responsible for responsible AI
Future-Proof Generative AI Capabilities
Manage Innovation without Losing Your Sanity
- Future Proofing
- Readiness
- Securely incorporate non-stop innovation
- Dramatically rising costs
- Why you need to unify
- Domino Cost Management Tools
Episode 5
Generative AI for Regulated Industries
- Why GenAI makes FSI/HLS leaders lose sleep
- Responsible Generative AI: The risks and how to mitigate them
- Data responsibility and privacy
- Fairness and explainability
Episode 6
Operationalizing GenAI Cost-effectively
- From PoCs and prototypes to production: Day 2 with Generative AI
- How to scale GenAI across the organization and standardize
Domino & Forrester
An expert’s guide to surviving and thriving as an AI leader in a GenAI world
- The role of leadership in driving value with AI/ML
- Building a diversified AI/ML product portfolio
- The opportunity and pitfalls of GenAI
- How to be responsible for Responsible AI
- Futureproofing for rapid technological change and upcoming AI regulation
Builder's Toolkit Series
Episode 1
Fine-tuning Large Language Models
- Review the motivation and theory behind PEFT (parameter-efficient fine-tuning) techniques
- Discover the power of quantization with the Hugging Face Trainer on Domino using Falcon-40b
- Investigate LoRA with the Falcon-7b LLM using PyTorch Lightning
Episode 2
Advanced Parameter Efficient Fine-tuning
- Learn about ZeRO and how it helps you fine-tune models across GPUs
- Using DeepSpeed and Ray to fine-tune GPTJ-6b model
- Load the model for inference onto a GPU or a CPU
Episode 3
Prompt Engineering Jumpstart
- Understand the structure of prompts.
- Help LLM understand what you need using zero-shot, one-shot, and few-shot in-context training.
- Work faster by reusing your prompts and leveraging open-source prompt template libraries.
- Explore automation techniques such as prompt chaining.
Episode 4
An Introduction to Retrieval-Augmented Generation (RAG)
- Theoretical Foundations: Natural Language Processing (NLP) basics and retrieval techniques.
- How RAG Works: RAG interactions in detail with real-world case studies.
- Applications and Challenges: Examine RAG-powered chatbots and search engines, and review RAG's limits.
- Demo & Q&A: We'll show you how RAG works in Domino and leave you time for Q&A at the end of the session.
Episode 5
Find the Right LLM for the Job
- The benefits and challenges of working with multiple LLM providers
- How Jupyter AI in Domino with your choice of LLM coder models to accelerate development (and documentation, too!)
- Why harnessing Domino's new AI Gateway can simplify the transition to best-of-breed LLM solutions.
Episode 6
Fine-tuning with PEFT using NVIDIA NeMo in Domino
- Learn how to leverage the NVIDIA NeMo Framework to accelerate large language model (LLM) training by up to 30%.
- Explore using NeMo Megatron, a powerful transformer, for fine-tuning on Domino's Enterprise AI Platform.
- Cover the entire model life cycle, from setup in Domino's AI Project Hub to customizing environments for fine-tuning and utilizing NeMo Megatron for text encoding and virtual token generation.
Bayer achieves "Science@Scale" with Domino
Bayer uses Domino to test more seed variants, producing more seed with less land, serving customers better while yielding additional company value.
"Domino has made it easier for users across the global enterprise, using different tools and with varied backgrounds and skill sets, to work with each other, leverage past work, and collaborate quickly."
Naveen Singla
Former Data Science Center of Excellence Lead, Bayer