The era of generative AI
Lukas Biewald, CEO and Co-Founder at Weights & Biases
We kicked off Fully Connected 2024 with a keynote from Weights &Biases CEO and Co-Founder Lukas Biewald. He shared his perspective on the generative AI industry: where we’ve come from, where we are today, and where we’re headed.
Building the generative AI industry
Shawn Lewis, CTO and Co-Founder at Weights & Biases
Hear from Shawn Lewis on the new Weights & Biases products and features for foundation model builders, enterprises building and fine-tuning their models, and software developers developing generative AI applications
How Meta trained Llama 3
Shawn Lewis, CTO and Co-Founder at Weights & Biases
We were thrilled that Joe Spisak, Product Director of GenAI at Meta, unveiled the latest family of Llama models, Llama 3, at Fully Connected.
Learn all about the training processes and alignment of Llama 3, which now ranks as the top-performing model in the open weights category on the MMLU, GSM-K, HumanEval benchmarks.
Overcoming the complexities of generative AI
Kari Briski, VP of Generative AI Software Product Management at NVIDIA
As models grow in complexity and scope, so does the challenge of training them. And few companies understand those challenges better than NVIDIA.
Join Kari Briski as she walks us through how the most innovative companies in the world are grappling with the challenges of training massive models for real-world use cases.
The future of trust in LLMs
Richard Socher, Founder & CEO at You.com
We’re currently editing this video. Please check back shortly.
Snowflake Copilot: Building the most powerful SQL LLM in the world
Vivek Raghunathan, VP of Engineering at Snowflake
Join Vivek Raghunathan to learn how Snowflake approaches building a powerful large language model for SQL, the challenges they faced along the way, and how they overcame them.
Generative AI: Scaling Adobe Firefly infrastructure and ML workflows
Ersin Yumer, Sr. Director of Engineering, AI/ML and Data at Adobe
At Adobe, hundreds of researchers and engineers work on large-scale generative AI models from initial research and prototyping to production and serving models at inference time for many applications to consume, including Adobe flagship products such as Photoshop.
In this talk, Ersin introduces how Adobe revamped and scaled their ML infrastructure and workflows to optimize for speed in research to production.
Getting started with large-scale training and LLMOps in Azure AI
Manash Goswami, Principal Group Program Manager at Microsoft
Azure AI is at the foundation of generative AI innovations you see today: OpenAI’s ChatGPT and Microsoft Copilots are all built on top of Azure AI platforms and tooling.
In this session, Manash covers a wide range of topics and best practices from large-scale training to build your LLMs and leverage pre-built LLMs from our model catalog. You’ll also learn how to get from prototype to production with LLMOps and new developer tools for iterative debugging, evaluation, deployment, and monitoring.
Deploying one of the first NVIDIA GH200 Grace Hopper Superchip Clusters in Lambda Cloud
David Hall, VP of NVIDIA Solutions, Lambda
Lambda introduced one of the first NVIDIA GH200 GPU clusters in its cloud, featuring NVIDIA’s new ARM-based Grace CPU for enhanced efficiency and coherent NVIDIA NVLink-C2C interconnect that provides 900 GB/s of bandwidth between the Grace CPU and Hopper GPU. The presentation will discuss Lambda’s GH200 cluster design optimized for ML training and our findings on training performance.
Understanding LLM performance in Snowflake using Weights & Biases and Snowpark Container Services
Vino Duraisamy, Developer Advocate, Snowflake at NVIDIA
We’re currently editing this video. Check back shortly.
Training recipes and scaling strategies for high-quality GenAI models
Natalia Vassilieva, VP and Field CTO, ML at Cerebras Systems
Training and serving GenAI models are expensive engineering and ML challenges. Even minor errors in model design or training procedure can result in significant waste of resources and in sub-optimal models.
Join Natalia as she shares how Cerebras trains large models and what they learned along the way. She shares her experience and insights from training various LLMs and multi-modal models, techniques for compute-efficient training of dense models, amd the benefits of sparse training and inference on Cerebras hardware.
AI 2024: The journey from here
Sri Viswanath, General Partner & Managing Director at Coatue
In this closing keynote, Sri gives an investor’s point of view about the state of AI today and where he expects our space to be in the next year. He looks at broad investment trends, the transition from research to deployment to business value, and forecasts the next big thing in AI.
NVIDIA and W&B fireside chat
Lukas Biewald, CEO and Co-Founder at Weights & Biases + Manuvir Das, VP of Enterprise Computing at NVIDIA
Join Manavir and Lukas for a candid conversation about the direction of machine learning as a space, why NVIDIA is investing so much in NIM, the partnership between NVIDIA and W&B, and what both men expect to see in the coming year in AI.
Closing thoughts
Robin Bordoli, CMO at Weights & Biases
We wrapped Fully Connected with some parting thoughts and hearty thank yous from our CMO Robin Bordoli. Find out who made the conference possible and when to book your tickets for 2025.
Thank you to our sponsors!
