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Verena Weber believes that GenAI is going to transform the way we work and interact with devices. Her mission is to help companies prepare for this transformation. She has strong expertise in NLP and over 7 years of experience in Machine Learning.

Huge thank you to  @zilliz  for sponsoring this episode. Zilliz - https://zilliz.com/

MLOps podcast #224 with Verena Weber, Generative AI Consultant at Verena Weber, GenAI in Production - Challenges and Trends.

// Abstract

The goal of this talk is to provide insights into challenges for Generative AI in production as well as trends aiming to solve some of these challenges. The challenges and trends Verena sees are: Model size and moving towards a mixture of experts architectures, context window - new breakthroughs for context lengths from unimodality to multimodality, next step large action models? regulation in the form of the EU AI Act. Verena uses the differences between Gemini 1.0 and Gemini 1.5 to exemplify some of these trends.

// Bio

Verena leverages GenAI in natural language to elevate business competitiveness and navigate its transformative impact. Her varied experience in multiple roles and sectors underpins her ability to extract business value from AI, blending deep technical expertise with strong business acumen. Post-graduation, she consulted in Data Science at Deloitte and then advanced her skills in NLP, Deep Learning, and GenAI as a Research Scientist at the Alexa team, Amazon. Passionate about gender diversity in tech, she mentors women to thrive in this field.

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Website: verenaweber.de

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Timestamps:

[00:00] AI Quality Conference

[01:33] Verena's preferred coffee

[02:15] Takeaways

[06:33] Ski Person of Influence

[11:31] Verena's background in the last 5-10 years

[14:24] Tech Evolution: Rapid Transformation

[18:13] Working at Amazon and key challenges

[20:10] Research-inspired suggestions

[22:21] AI Updates Impact Workflows

[22:52] Alexa Query Distribution Analysis

[24:06] Innovative Solutions for Alexa

[25:27] Robust T5 Data Prompting

[27:38] Audio Data Quality Challenges

[28:21-29:28] Zilliz ad

[29:28] Alexa data transcription and data cleaning

[35:38] Considering needs, costs, and complexity

[37:44] ChatGPT is not ideal for classification

[39:32] Comparison of model building using TF, IDF

[45:08] Struggle to boost diversity in conference speakers

[47:30] Creating safe environments helps underrepresented individuals participate

[48:29] Wrap up