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World Congress 2024 • Aug 20, 2024 • Session details

Developer Experience, Platform Engineering and AI powered Apps

Ignacio Riesgo , Natale Vinto

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.

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#1 about 3 min

Navigating the generative AI transition

Breaking down the complex landscape of artificial intelligence into manageable areas of focus reveals a critical need for structured teamwork.

#2 about 3 min

Evaluating foundation models for business use

Key criteria for choosing between open and closed models include bias prevention, legal compliance, and measurable business value.

#3 about 2 min

Prioritizing AI use cases and model selection

Early successes in risk and supply chain inform deployment decisions balancing model performance, speed, cost, and legal indemnification.

#4 about 3 min

Augmenting base models with enterprise data

Addressing model inaccuracy and intellectual property risks requires integrating proprietary company data into transparent foundation models through synthetic generation.

#5 about 2 min

Evolving roles in AI driven software teams

The emergence of new skills enables collaborative workflows connecting data engineers and citizen data scientists.

#6 about 3 min

Managing the evolving AI developer stack

Teams prioritize specific technical domains or lifecycle areas to manage the intimidating complexity of emerging machine learning libraries.

#7 about 3 min

Bringing machine learning into application DevOps

Combining application code patterns with model training evaluation produces a modern operations flow supported by targeted enterprise platform tools.

#8 about 6 min

Starting a data science project with notebooks

A data scientist provisions a notebook environment to test a standard open-source image generation format before adding proprietary context.

#9 about 5 min

Fine-tuning and serving custom AI models

Automation pipelines process specific datasets to refine output behaviors and publish the resulting formats as queryable network endpoints.

#10 about 5 min

Connecting enterprise applications to model APIs

Internal developer portals rapidly scaffold repositories and deployment configurations that consume newly trained model endpoints inside functional frontend applications.

Matching moments

3:13 min

Embedding generative AI in enterprise software platforms

Mike Butcher Mike Butcher +3 · WWC 2024

3:55 min

Scaling enterprise developer ecosystems in the AI era

Thomas Jung Thomas Jung +1 · WWC Europe 2026

2:50 min

Transitioning from deep learning models to foundation software

Marcel Scherenberg Marcel Scherenberg · WWC 2025

2:08 min

Essential engineering roles in the generative AI space

Mary Grygleski Mary Grygleski · LIVE

1:56 min

Scaling AI adoption to non-traditional enterprise developers

Neel Sundaresan Neel Sundaresan +1 · WWC Europe 2026

3:03 min

Career evolution in data engineering and AI platforms

Maria Apazoglou · Coffee With Developers

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