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World Congress 2023 • Aug 11, 2023

How E.On productionizes its AI model & Implementation of Secure Generative AI.

Kapil Gupta

Struggling to move AI from proof-of-concept to production? Discover how E.ON leverages data as code and robust LLM guards to securely scale enterprise generative AI in weeks.

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#1 about 4 min

Roleplaying capabilities of generative AI for customer engagement

Using language models to simulate roleplay demonstrates unconventional methods for creating interactive customer experiences.

#2 about 2 min

Understanding daily machine learning operations challenges and personas

The infrastructure and deployment struggles faced by data scientists transitioning experimental iterations to production require efficient automation.

#3 about 2 min

Implementing data as code to abstract data infrastructure

Adopting semantic versioning and customized libraries enables engineers to seamlessly import verified data frames without manual verification.

#4 about 3 min

Querying metadata sources using large language models

Applying text algorithms to map internal data warehouses generates automated query logic efficiently for analytics users.

#5 about 2 min

Powering website search queries with generative language algorithms

Replacing static link-based search fields with conversational interfaces driven by deep knowledge bases improves user discoverability.

#6 about 2 min

Navigating sprawling codebases using generative artificial intelligence tools

Stacking code embeddings securely with vector databases accelerates code comprehension and legacy debugging workflows.

#7 about 2 min

Transcribing and analyzing customer service calls for sentiment

Processing audio conversations into readable text via cognitive APIs extracts sentiment profiles and compliance tracking safely.

#8 about 2 min

Automating incoming customer email processing through intent classification

Evaluating high-volume customer emails systematically categorizes direct requests to prevent manual workload bottlenecks.

#9 about 2 min

Combining unstructured enterprise data with cognitive search tools

Modulating response temperatures alongside extracted internal documentation enables specialized answers detailing specific service discounts.

#10 about 2 min

Protecting production language models from hallucination and hacking

Deploying dedicated security guardrails strictly filters toxic language and limits endpoint vulnerabilities better than superficial prompt checks.

#11 about 2 min

Projecting future foundation model use cases for sustainability

Unifying predictive smart meter outputs empowers modern efforts targeting scalable carbon emission reductions and hyper-personalization.

Matching moments

2:52 min

Scaling generative AI use cases across large enterprises

Mike Butcher Mike Butcher +3 · WWC 2024

1:39 min

Balancing human-centric AI collaboration with environmental sustainability practices

Madalena Costa Madalena Costa · WWC 2024

2:08 min

Essential engineering roles in the generative AI space

Mary Grygleski Mary Grygleski · LIVE

3:13 min

Integrating state-of-the-art generative models and automated processing

Kapil Gupta Kapil Gupta · WWC 2025

7:28 min

Accelerating product features using generative large language models

David Singleton David Singleton +1 · Coffee With Developers

3:13 min

Embedding generative AI in enterprise software platforms

Mike Butcher Mike Butcher +3 · WWC 2024

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