Kapil Gupta
How E.On productionizes its AI model & Implementation of Secure Generative AI.
#1about 2 minutes
Defining the roles of data scientists and MLOps engineers
MLOps engineers build secure, automated infrastructure to help data scientists move models from proof-of-concept to production in weeks instead of months.
#2about 2 minutes
Implementing the data as code concept for ML
E.On uses a "data as code" approach with versioned Python libraries to provide data scientists with abstracted, quality-checked data frames.
#3about 3 minutes
Using LLMs to discover datasets and manage metadata
LLMs can query enterprise metadata, helping users find relevant data sources and even generate SQL queries through natural language conversations.
#4about 1 minute
Powering website search with generative AI
E.On is replacing traditional keyword search on its website with a GPT-powered system that provides crisp, human-like answers based on a broad knowledge base.
#5about 2 minutes
Using LLMs to understand and navigate codebases
Developers can use a combination of LangChain, vector databases, and OpenAI to ask natural language questions about a large codebase for faster debugging.
#6about 1 minute
Architecture for analyzing contact center call recordings
An architecture using speech-to-text and Azure OpenAI analyzes customer call recordings to extract sentiment, verify GDPR consent, and uncover business insights.
#7about 2 minutes
Automating email processing with intent classification
To manage millions of customer emails, an automated system uses OpenAI for intent classification to suggest accurate and fast responses for call center agents.
#8about 3 minutes
Securely connecting generative AI to enterprise data
An architecture combining a knowledge base, cognitive search, and embeddings allows GPT to securely answer questions using private enterprise data, protected by LLM guards.
#9about 1 minute
Overview of generative AI applications at E.On
A summary of key application areas for foundation models, including real-time carbon footprinting, hyper-personalization, and smart data analysis.
Related jobs
Jobs that call for the skills explored in this talk.
ROSEN Technology and Research Center GmbH
Osnabrück, Germany
Senior
TypeScript
React
+3
Wilken GmbH
Ulm, Germany
Senior
Kubernetes
AI Frameworks
+3
msg
Ismaning, Germany
Intermediate
Senior
Data analysis
Cloud (AWS/Google/Azure)
Matching moments
02:06 MIN
The rise of MLOps and AI security considerations
MLOps and AI Driven Development
05:39 MIN
Understanding the GenAI lifecycle and its operational challenges
LLMOps-driven fine-tuning, evaluation, and inference with NVIDIA NIM & NeMo Microservices
04:28 MIN
Understanding the key challenges in operationalizing GenAI projects
From Traction to Production: Maturing your GenAIOps step by step
04:51 MIN
Overcoming the challenges of productionizing AI models
Navigating the AI Revolution in Software Development
04:59 MIN
Introducing the Azure AI platform for end-to-end LLMOps
From Traction to Production: Maturing your LLMOps step by step
01:47 MIN
Three pillars for integrating LLMs in products
Using LLMs in your Product
01:06 MIN
Moving beyond hype with real-world generative AI
Semantic AI: Why Embeddings Might Matter More Than LLMs
03:02 MIN
The future of AI in DevOps and MLOps
Navigating the AI Wave in DevOps
Featured Partners
Related Videos
DevOps for AI: running LLMs in production with Kubernetes and KubeFlow
Aarno Aukia
From Traction to Production: Maturing your GenAIOps step by step
Maxim Salnikov
The State of GenAI & Machine Learning in 2025
Alejandro Saucedo
GenAI Security: Navigating the Unseen Iceberg
Maish Saidel-Keesing
The state of MLOps - machine learning in production at enterprise scale
Bas Geerdink
Beyond GPT: Building Unified GenAI Platforms for the Enterprise of Tomorrow
Kapil Gupta
From Traction to Production: Maturing your LLMOps step by step
Maxim Salnikov
Using LLMs in your Product
Daniel Töws
Related Articles
View all articles.gif?w=240&auto=compress,format)
.gif?w=240&auto=compress,format)


From learning to earning
Jobs that call for the skills explored in this talk.

Media Gmbh
Ingolstadt, Germany
Intermediate
DevOps
Python
Docker
Terraform
Kubernetes
+3

Allianz Group
Municipality of Madrid, Spain
Remote
GIT
JSON
YAML
Azure
+7







OpenAI
München, Germany
Senior
API
Python
JavaScript
Machine Learning