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# Developer Experience, Platform Engineering and AI powered Apps

How do you build accurate AI apps when public models lack your enterprise data? Learn how platform engineering turns complex ML infrastructure into a simple, callable service.

- **Speakers:** [Ignacio Riesgo](https://www.wearedevelopers.com/@ignacio-riesgo), [Natale Vinto](https://www.wearedevelopers.com/@natale-vinto)
- **Event:** World Congress 2024
- **Published:** August 20, 2024
- **Duration:** 31:38
- **URL:** https://www.wearedevelopers.com/videos/990-developer-experience-platform-engineering-and-ai-powered-apps

## Summary

The integration of generative AI into enterprise applications has fundamentally shifted developer experience, forcing organizations to navigate a complex new stack. While foundation models introduce immense potential, less than 1% of enterprise data is represented in these public models, making internal context mapping a critical challenge. Engineering teams must carefully select transparent base models while balancing business ROI against the two biggest risks of enterprise AI: model inaccuracy and intellectual property management. Overcoming these hurdles requires a deliberate approach to "bring your own data," ensuring that AI tooling respects compliance, legal frameworks, and commercial indemnification.

To bridge the gap between foundation models and practical application, the traditional software development flow is merging with machine learning lifecycles to create a unified ML Ops pipeline. This evolution blurs the lines between IT operations, developers, and the emerging role of the "citizen data scientist"—business experts who possess the deeper domain knowledge required to inform model behavior. Teams are structuring enterprise knowledge using taxonomies, generating synthetic data, and fine-tuning open source models to produce highly accurate, domain-specific responses. 

Scaffolding internal developer platforms acts as the connective tissue for these AI-powered applications. By leveraging frameworks like backstage alongside open source ML tools, platform engineers can automate software templates that abstract away the complexity of ML infrastructure. A practical workflow demonstrates how data scientists can fine-tune a model via Jupyter notebooks, serve it via an API endpoint, and allow developers to easily connect user-facing applications to that inference mechanism. Ultimately, mastering this modern stack allows developers to transition from treating AI as an overwhelming landscape to treating it as just another callable service within well-established deployment workflows.

**Keywords:** developer experience, platform engineering, AI application development, foundation model selection, synthetic data generation, ML ops unified pipeline, citizen data scientist, AI intellectual property risks, backstage software templates, model fine-tuning workflow, API inference integration, internal developer platform, jupyter notebook automation, open source AI models, enterprise AI compliance

## Chapters

1. **Navigating the generative AI transition** (00:57) — Breaking down the complex landscape of artificial intelligence into manageable areas of focus reveals a critical need for structured teamwork.
1. **Evaluating foundation models for business use** (03:21) — Key criteria for choosing between open and closed models include bias prevention, legal compliance, and measurable business value.
1. **Prioritizing AI use cases and model selection** (05:48) — Early successes in risk and supply chain inform deployment decisions balancing model performance, speed, cost, and legal indemnification.
1. **Augmenting base models with enterprise data** (07:35) — Addressing model inaccuracy and intellectual property risks requires integrating proprietary company data into transparent foundation models through synthetic generation.
1. **Evolving roles in AI driven software teams** (10:16) — The emergence of new skills enables collaborative workflows connecting data engineers and citizen data scientists.
1. **Managing the evolving AI developer stack** (12:16) — Teams prioritize specific technical domains or lifecycle areas to manage the intimidating complexity of emerging machine learning libraries.
1. **Bringing machine learning into application DevOps** (14:49) — Combining application code patterns with model training evaluation produces a modern operations flow supported by targeted enterprise platform tools.
1. **Starting a data science project with notebooks** (17:36) — A data scientist provisions a notebook environment to test a standard open-source image generation format before adding proprietary context.
1. **Fine-tuning and serving custom AI models** (23:14) — Automation pipelines process specific datasets to refine output behaviors and publish the resulting formats as queryable network endpoints.
1. **Connecting enterprise applications to model APIs** (27:20) — Internal developer portals rapidly scaffold repositories and deployment configurations that consume newly trained model endpoints inside functional frontend applications.

## Related Moments

- [Embedding generative AI in enterprise software platforms](https://www.wearedevelopers.com/videos/916-beyond-the-hype-real-world-ai-strategies-panel) (from "Beyond the Hype: Real-World AI Strategies Panel")
- [Scaling enterprise developer ecosystems in the AI era](https://www.wearedevelopers.com/videos/100201-evolving-the-developer-experience-in-the-age-of-ai) (from "Evolving the developer experience in the age of AI")
- [Transitioning from deep learning models to foundation software](https://www.wearedevelopers.com/videos/1533-infrastructure-as-prompts-creating-azure-infrastructure-with-ai-agents) (from "Infrastructure as Prompts: Creating Azure Infrastructure with AI Agents")
- [Essential engineering roles in the generative AI space](https://www.wearedevelopers.com/videos/844-enter-the-brave-new-world-of-genai-with-vector-search) (from "Enter the Brave New World of GenAI with Vector Search")
- [Scaling AI adoption to non-traditional enterprise developers](https://www.wearedevelopers.com/videos/100256-can-this-elephant-dance-ibm-bob-and-the-future-of-ai-first-software-development) (from "Can This Elephant Dance? IBM Bob and the Future of AI-First Software Development")
- [Career evolution in data engineering and AI platforms](https://www.wearedevelopers.com/videos/1209-coffee-with-developers-maria-apazoglou) (from "Coffee with Developers - Maria Apazoglou")

## Related Articles

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- [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it)
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