Generative AI in Production: Best Practices and Lessons Learned
Meryem Arik: Meryem is a recovering physicist, co-founder, and CEO of TitanML.TitanML is an NLP development platform that focuses on the deployability of LLMs - allowing businesses to build smaller and cheaper language model deployments easily.
Greg Loughnane is a Generative AI and LLM educator, community builder, CEO, and Founder of AI Makerspace, where he’s building useful onramps for people looking to join the unautomatable workforce of the future.
Alessya Visnjic is the CEO of WhyLabs, the market leader in AI observability. AI teams at Square and Glassdoor use the WhyLabs platform to ensure AI models, from classification to LLMs, generate accurate, fair, and safe customer experiences.
Chris Alexiuk, aka The LLM Wizard and educator extraordinaire. Chris was previously an Instructor and curriculum developer at FourthBrain and is currently a founding ML Engineer at Ox and CTO at AI Makerspace.
Hannes Hapke is a principal machine learning engineer at Digits . He implemented deep learning systems from inception to production and has authored two books: building ml pipelines and NLP in action.
Ville Tuulos is the co-founder and CEO of Outerbounds, a human-centric platform for data, ML, and AI projects based on metaflow.org, an open-source project he started while working on ML Infra and Architecture at Netflix.
Questions
What advice would you give someone looking to build a new generative AI-powered product or feature? What steps would you advice someone to take to understand its feasibility for real-world applications? (Panelists to ask: Ville, Hannes, Chris, Greg)
What are the most common challenges when translating a generative AI product/feature prototype into a production-ready product? (Panelists to ask: Ville, Chris, Greg, Alessya)
Can you share a war story where translating a generative AI-powered prototype to production posed unexpected challenges and how you overcame them? (Panelists to ask: Hannes)
Are specific tools, platforms, or infrastructure indispensable when moving from prototype to production, specifically for generative AI-powered products or features? (Panelists to ask: Ville, Chris, Alessya)
How do you address scalability when implementing generative AI models in production, especially when the original research might have been conducted on smaller datasets? (Panelists to ask: Hannes)
What measures do you take to ensure that a generative AI model, which works well in a controlled research environment, is robust and reliable when deployed in real-world scenarios? (Panelists to ask: Hannes, Alessya)
With the rapid advancements in generative AI research, where do you see the future of its implementation in production heading? (Panelists to ask: Chris, Greg)
What advice would you give aspiring practitioners looking to bridge the skill gap between building generative AI toy examples in a notebook to production implementation? (Panelists to ask: Ville, Greg)