Optimized AI Conference 2025 | Wix | 14 April 2025 | TBA, Atlanta, GA, USA

Learn directly from 80+ leading AI companies, VCs, executive AI leadership, and senior engineers across 12 segments to shorten your path to production AI.

The conference is one of the most practical AI events you can attend. The organizers of the event both work for top AI companies NVIDIA/Google so we curate a breadth of speakers that we would want to learn from in order to make progress with our production workloads.

The content strikes a healthy balance between deep theory required for extending industrial research, deep dives on production use-cases and business optimization strategies. We believe that every AI professional can benefit from developing both their technical and business development skills.

Come accelerate your learning, generate revenue and advance your career.

Topics:
• Knowing the foundations of deep learning has it benefits. How do you scale your training in a cost-effective manner. When it comes to deploying deep learning. models, methods such as quantization and pruning can help to reduce inference costs and latency. We explore optimizations on both the training and inference lifecycle.
• Many teams have already implemented RAG systems to keep their insight generation engines up-to-date. How do you increase the accuracy and quality of results coming from these systems? Which component do you scale, and how can you keep inference costs as low as possible but still get the highest quality results?
• The current GEN AI adoption curve is just beginning. The true frontier of breakthrough applications will come from training and learning on more diverse datasets. Image, sound, and video generation will be at the top of the list, showcasing how to fine-tune and deploy these various types of models.
• Working for top AI companies is not the only way to earn a great living from the Gen AI boom. Many enterprises need the Capex to expand their teams and hire top-tier technical talent. Now is a rare opportunity to build the next generation of consulting firms, that are co-pilot enabled.
• Starting an AI company is easier than ever. This makes competition fierce and differentiating your unique value proposition takes advanced positioning, timing and precision marketing to preserve budgets. We’ll be hosting a number of startups that you can join and learn from.
• What remains constant in the GEN AI age? Growing volumes of data. This means the cost to process medium to large datasets will only increase over time. We explore the field of accelerated data processing on GPUs and other optimized platforms to reduce processing costs and increase the quality and speed of insights from your datasets. Learn how to accelerate spark, pandas and ML workloads.
• LLM inference is distinctly different from other types of model inference. We explore both traditional approaches to model optimization and deployment and this new field of deploying token, image, and video-generating systems. The goal is to minimize cost and maximize throughput and utilization of these systems.
• What is your career optimization function? Is it wealth, freedom, control of time, or interesting work? How do you navigate this new age of GEN AI work, and how do you position yourself to advance your career strategically? Learn from many experts, both their strategies and mistakes, so you can optimize your next career move.
• This is the true frontier of the LLM revolution. When agents are fully online, small companies can compete with large companies due to an AI-accelerated labor force. This paradigm will transform businesses in a meaningful way, enabling organizations to scale their efforts. Individuals who follow the Company-of-One philosophy can create viable businesses with no employees, by leveraging agents.
• All leadership must understand AI at its core in order to lead the next generation companies. By going deep into how these systems work, leaders can have more powerful predictive capabilities that they can leverage to steer their organizations into the rapidly changing landscape. Leadership is not easy, and AI Leadership is even harder, given how fast the field is evolving, come compress your learning.
• GEN AI startups have some of the highest valuations and exits in history. Many small firms are being acquired for hundreds of millions. Most of these companies raised funding along the way to accelerate their product development and market penetration efforts. Learn how to connect with VCs and better understand what the funding path entails.
• The biggest barrier to adoption of GEN AI in enterprise is the cost and effectiveness of inference. We explore many topics on scaling out these systems effectively, leveraging routers to choose the best models between frontier and open-source models. You will leave learning how to deploy your own GEN AI applications cost effectively.

2024 Past Speakers

Chip Huyen
VP of Data Science & OSS | Oreilly Author, Voltron Data

Sebastian Raschka, PhD
Staff Research Engineer, Lightning AI

Paige Bailey
Gen AI Developer Relations Lead, Google DeepMind

Aishwarya Srinivasan
Sr. AI Advisor, Microsoft

Joseph Spisak
Product Director & Head of Generative AI Open Source, Meta

Mike Tamir, PhD
Distinguished ML Scientist, Shopify

Laura Edell
Chief Data Scientist, AI Markets and Innovation, Microsoft

Joel Grus
Director and VP, AI/ML and Emerging Technologies, Capital Group

Hamza Farooq
Founder | Sr. Research Science Manager, Traversaal.ai

François Chollet
Software Engineer | Creator of Keras, Google

Julien Simon
Chief Evangelist, Hugging Face

Josh Patterson
CEO, Voltron Data

Bojan Tunguz, PhD
Sr. Systems Software Engineer, Ex-NVIDIA

Mark Moyou, PhD
Sr. Data Scientist/Solutions Architect, NVIDIA

Raja Iqbal
Founder and CEO, Data Science Dojo

Sam Partee
Principal AI Engineer, ex-Redis

Vijay Reddy
Staff Machine Learning Engineer, Google

Liangjie Hong
Director of Engineering, LinkedIn

Microsoft
Alicia Frame, PhD
Principal Product Manager - Azure OpenAI

Garnet S. Heraman
Managing Partner, Aperture

Thomas Dohmke
Director of Machine Learning, Instacart

Mike Seid
Engineering Lead - ML Platform, Spotify

David Talby
CTO, John Snow Labs

Suraj Subramanian
Machine Learning Engineer, Meta

Trey Grainger
Founder | Author, SearchKernel | Packt

Trey Grainger
Product Lead, Google AI Studio, Google

Sarfaraz Hussein, PhD
Sr. ML Scientist/Engineer, Motional

Kiran Nisar
ML Engineer, Weights and Biases

Anyi Wang
Engineering Manager, Quora

Aayush Mudgal
Staff Machine Learning Engineer, Pinterest

Sanjay Agravat
Staff Software Engineer, CTO Office, Google Cloud

Chris Lattner
CEO, Modular

Vani Mandava
Head of Engineering, University of Washington

Murium Iqbal
Staff Applied Scientist, Etsy

Benika Hall, PhD
Sr. Solutions Architect, NVIDIA

Alex Vayner
Managing Partner, Martingale Insights

Beverly Wright, PhD CAP
VP Data Science & AI/CAIO, Wavicle Data Solutions

Reshma Lal Jagadheesh
Senior Manager, Artificial Intelligence, Salesforce

Joshua Goldstein
Solutions Engineer, Seldon

Jaya Kawale
VP of Engineering, Tubi

Katie (Porter) Roberts
Data Science Solutions Architect, Neo4j

Braden Hancock
Director of AI, Meta

Bill Franks
Director, Center for Data Science & Analytics, Kennesaw State University

Rosie Min
AI Consultant, Google

Sujatha Sundararaman
Head of Search, Understanding and Voice, YTM at Google, Google

Dennis Ramdass
Founding Engineer, Tako

Xiquan Cui
Sr. Manager Machine Learning, The Home Depot

Ashim Datta
Sr. Machine Learning Manager, Apple

Marcus Eagan
Advisor, Weaviate

Aditya Sthanunathan
Director of Product Management, The Home Depot

Mo Medwani, PhD
CEO & Founder, Innovatics

Aaron Richter
Data Engineer, Robinhood

Divyashree Sreepathihalli
Sr. Machine Learning Engineer, Google

Roie Schwaber-Cohen
Staff Developer Advocate, Pinecone

Surya Kanoria
Machine Learning Engineer, Spotify

Shilpa Kancharla
Machine Learning Engineer, Google

Ugur Kursuncu
Assistant Professor, Georgia State University

Marcelle Bonterre
Sr. Machine Learning Engineer, VMWare

Stephane Pinel
Sr. Director of AI, Axios HQ