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

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.

- **Speakers:** [Kapil Gupta](https://www.wearedevelopers.com/@kapil-gupta)
- **Event:** World Congress 2023
- **Published:** August 11, 2023
- **Duration:** 19:27
- **URL:** https://www.wearedevelopers.com/videos/623-how-e-on-productionizes-its-ai-model-implementation-of-secure-generative-ai

## Summary

E.ON's MLOps team tackles the friction between data scientists and engineers by transitioning from traditional fragile pipelines to a 'data as code' model. Utilizing semantic versioning and reusable Python libraries, teams can abstract away data verification and move models from proof-of-concept to production in weeks rather than months. Expanding out into LLMOps, the engineering team leverages generative AI to dismantle massive organizational data silos via explicit 'talk to your data' interfaces. By implementing large language models to manage metadata, systems can suggest database tables and autonomously generate SQL queries to accelerate data discovery. Simultaneously, developers utilize LangChain, vector databases, and OpenAI embeddings to natively 'talk to their code,' allowing engineers to map codebase dependencies, locate bugs, and speed up onboarding without endless knowledge transfer sessions. Applying these principles to enterprise operations, E.ON powers its customer web search with enterprise-grounded GPT models to prevent vague link returns, favoring explicit answers extracted from internal databases and PDF contracts. Furthermore, they automate intent classification for millions of customer emails and analyze contact center interactions via the Azure OpenAI API for sentiment analysis and GDPR marketing compliance. Acknowledging that prompt engineering alone is insufficient for robust production environments, E.ON actively deploys proprietary LLM guards. These automated evaluation loops continuously monitor and block prompt injections, toxic language, and model hallucinations, ensuring highly secure and trustworthy generative AI deployments entirely at scale.

**Keywords:** mlops infrastructure deployment, data as code implementation, llm metadata management, generative ai productionization, langchain vector databases, codebase debugging embeddings, azure openai integration, contact center sentiment analysis, automated intent classification, llms security guardrails, prompt injection prevention, enterprise data grounding, semantic versioning pipelines, compliance gdpr automation, ai hallucination mitigation

## Chapters

1. **Roleplaying capabilities of generative AI for customer engagement** (00:00) — Using language models to simulate roleplay demonstrates unconventional methods for creating interactive customer experiences.
1. **Understanding daily machine learning operations challenges and personas** (03:11) — The infrastructure and deployment struggles faced by data scientists transitioning experimental iterations to production require efficient automation.
1. **Implementing data as code to abstract data infrastructure** (04:41) — Adopting semantic versioning and customized libraries enables engineers to seamlessly import verified data frames without manual verification.
1. **Querying metadata sources using large language models** (06:42) — Applying text algorithms to map internal data warehouses generates automated query logic efficiently for analytics users.
1. **Powering website search queries with generative language algorithms** (09:41) — Replacing static link-based search fields with conversational interfaces driven by deep knowledge bases improves user discoverability.
1. **Navigating sprawling codebases using generative artificial intelligence tools** (11:04) — Stacking code embeddings securely with vector databases accelerates code comprehension and legacy debugging workflows.
1. **Transcribing and analyzing customer service calls for sentiment** (12:39) — Processing audio conversations into readable text via cognitive APIs extracts sentiment profiles and compliance tracking safely.
1. **Automating incoming customer email processing through intent classification** (14:01) — Evaluating high-volume customer emails systematically categorizes direct requests to prevent manual workload bottlenecks.
1. **Combining unstructured enterprise data with cognitive search tools** (15:37) — Modulating response temperatures alongside extracted internal documentation enables specialized answers detailing specific service discounts.
1. **Protecting production language models from hallucination and hacking** (16:41) — Deploying dedicated security guardrails strictly filters toxic language and limits endpoint vulnerabilities better than superficial prompt checks.
1. **Projecting future foundation model use cases for sustainability** (18:09) — Unifying predictive smart meter outputs empowers modern efforts targeting scalable carbon emission reductions and hyper-personalization.

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