Ignacio Riesgo, Natale Vinto

Developer Experience, Platform Engineering and AI powered Apps

Platform engineering is the crucial bridge between data science and app development, creating a golden path for shipping AI-powered applications.

Developer Experience, Platform Engineering and AI powered Apps
#1about 2 minutes

Navigating the overwhelming wave of generative AI adoption

The rapid rise of generative AI requires breaking down complex problems and fostering team collaboration to manage the challenges.

#2about 4 minutes

How to choose the right foundation model for your business

Selecting a foundation model involves balancing open versus closed source options while addressing critical questions from compliance, legal, and business stakeholders.

#3about 3 minutes

Improving model accuracy by using your own enterprise data

Incorporating your unique enterprise data into foundation models is essential for overcoming inaccuracy and managing intellectual property risks.

#4about 2 minutes

Understanding the new roles in AI-powered development teams

The shift to AI introduces new roles like citizen data scientists and creates overlapping responsibilities between data scientists, developers, and platform engineers.

#5about 3 minutes

Understanding the new AI developer stack and MLOps workflow

The modern AI development process combines the traditional developer loop with a new data and machine learning flow, creating a comprehensive MLOps cycle.

#6about 2 minutes

Using Red Hat tools across the AI development lifecycle

Red Hat's portfolio, including RHEL AI, InstructLab, and OpenShift AI, provides a comprehensive toolset for model builders, developers, and platform engineers.

#7about 6 minutes

Demo of a data scientist's workflow in OpenShift AI

A data scientist uses Jupyter Notebooks within OpenShift AI to download a base model like Stable Diffusion from Hugging Face and perform initial tests.

#8about 3 minutes

Demo of fine-tuning a model with custom data

The base model is fine-tuned with custom image data and the entire training process is automated using a Kubeflow pipeline for consistency and repeatability.

#9about 4 minutes

Demo of scaffolding an AI app with Developer Hub

Red Hat Developer Hub, based on Backstage, uses software templates to automatically scaffold a new application, including the repository, CI/CD pipeline, and connection to the model's API.

#10about 2 minutes

Demo of the final context-aware generative AI application

The final application successfully uses the fine-tuned model via its API to generate custom, context-aware images, completing the end-to-end MLOps workflow.

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