ai 
Data Engineering Podcast
APRIL 21, 2024
Summary Generative AI has rapidly transformed everything in the technology sector. When AI started gaining adoption he realized that he had even more potential for a transformative experience. How do you manage the personalization of the AI functionality in your system for each user/team?
The Pragmatic Engineer
MARCH 19, 2024
There’s been a number of news stories about “AI developers,” recently. The company says: “Devin is the new state-of-the-art on the SWE-Bench coding benchmark, has successfully passed practical engineering interviews from leading AI companies, and has even completed real jobs on Upwork.
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Snowflake
APRIL 11, 2024
That’s why we prioritize simplicity, governance and quality in everything we build – including our AI-powered tools. This unique synergy pushes the boundaries of data exploration, empowering you with an unparalleled AI assistant. Snowflake’s AI research team is at the forefront, exploring the intricacies of building a powerful SQL LLM.
Data Engineering Podcast
MARCH 3, 2024
Colleen Tartow has worked across all stages of the data lifecycle, and in this episode she shares her hard-earned wisdom about how to conduct an AI program for your organization. From March 26-28th 2024, we'll play host to hundreds of attendees, 100 top speakers and dozens of startups that are advancing data science, engineering and AI.
Speaker: Aindra Misra, Sr. Staff Product Manager of Data & AI at BILL (Previously PM Lead at Twitter/X)
We’ll explore how these applications are transforming with the introduction of Gen AI, and discuss the anticipated use cases for 2024 and beyond. Key objectives: Introduction to the structures and ownership dynamics of data platform, analytics and AI teams, along with an exploration of various roles in the data ecosystem.
Snowflake
APRIL 24, 2024
Members of the Snowflake AI Research team pioneered systems such as ZeRO and DeepSpeed , PagedAttention / vLLM , and LLM360 which significantly reduced the cost of LLM training and inference, and open sourced them to make LLMs more accessible and cost-effective for the community. Daily limits apply. Chat with Arctic!
Data Engineering Podcast
OCTOBER 1, 2023
Now that AI has reached the level of sophistication seen in the various generative models it is being used to build new ETL workflows. In this episode Jay Mishra shares his experiences and insights building ETL pipelines with the help of generative AI. What are some of the types of errors that you are likely to experience from the AI?
Speaker: Shreya Rajpal, Co-Founder and CEO at Guardrails AI & Travis Addair, Co-Founder and CTO at Predibase
Putting the right LLMOps process in place today will pay dividends tomorrow, enabling you to leverage the part of AI that constitutes your IP – your data – to build a defensible AI strategy for the future.
Speaker: Tony Karrer, Ryan Barker, Grant Wiles, Zach Asman, & Mark Pace
We'll walk through two compelling case studies that showcase how AI is reimagining industries and revolutionizing the way we interact with technology. Don't miss out on this opportunity to stay ahead of the AI curve! Save your seat today and be part of the tech conversation that's shaping the future.
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Speaker: Dr. Greg Loughnane and Chris Alexiuk
Technology professionals developing generative AI applications are finding that there are big leaps from POCs and MVPs to production-ready applications. However, during development – and even more so once deployed to production – best practices for operating and improving generative AI applications are less understood.
Speaker: Aarushi Kansal, AI Leader & Author and Tony Karrer, Founder & CTO at Aggregage
Aarushi Kansal, AI leader, is here to explore ongoing testing and evaluation strategies tailored specifically for LLM-based applications. To effectively create reliable LLM-based (often with RAG) applications, extensive testing and evaluation processes are crucial. This often ends up involving meticulous adjustments to prompts.
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“Reimagined: Building Products with Generative AI” is an extensive guide for integrating generative AI into product strategy and careers featuring over 150 real-world examples, 30 case studies, and 20+ frameworks, and endorsed by over 20 leading AI and product executives, inventors, entrepreneurs, and researchers.
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