The Docker extension for GitHub Copilot, available today in the GitHub Marketplace, helps to generate portable applications and the cloud native components that keep them running smoothly. Join Derek McGowan, software engineer at Docker, to learn how Docker empowers developers to focus on innovation—closing the gap from first lines of code to production—by standardizing best practices and enabling integrations with tools like Copilot. Using preconfigured workflows for CI/CD pipelines ensures that updates to AI models or code are automatically tested and deployed and reduces manual intervention. Come and explore how this automation supports ongoing efficiency and improvements in AI models and systems.
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Overview
Docker helps developers bring their ideas to life by conquering the complexity of app development. Actively used by millions of developers around the world, Docker Desktop and Docker Hub provide unmatched simplicity, agility and choice.
Videos
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live
Sessions
Product Demo
AI
Level 200: Intermediate
Applicable to all
Copilot
Enterprise - Engineering Leadership
Enterprise - Developers
Open Source Developers or Maintainers
In-person
Recorded
On-demand
AI
Automated Infrastructure Deployment
Cloud
Software Engineering
Collaboration
Productivity
AI
AI
Yes
AI Combo
Product Demo
Generate portable cloud native applications and automate CI/CD workflows to streamline updates and boost development efficiency.
Simplify infrastructure management and shift focus to scaling AI/ML solutions while ensuring consistency across environments, from local development to cloud deployment.
https://youtu.be/_ilPX6BnZOM
Tuesday, Oct 29
1:00 p.m. Tuesday, Oct 29
Discussion Lounge
Level 200: Intermediate
Applicable to all
Extensibility/Ecosystem
Enterprise - Engineering Leadership
Enterprise - Developers
Open Source Developers or Maintainers
Startups
In-person
AI
Cloud
Containerized Applications
Open Source
Software Engineering
Collaboration
Productivity
AI
AI
Yes
AI & Dev Ex
Discussions Lounge
Learn how to eliminate the challenges that using fragmented tools and configurations present, and speed up AI development and deployment.
See how developers can iterate faster and seamlessly transition between stages of AI workflows to improve efficiency and reduce errors.
Discover how to streamline your transition from local development to cloud-based model training for AI applications.
3:00 p.m. Tuesday, Oct 29
Tuesday, Oct 29
In this audience-driven discussion, developers and technical leaders will explore key challenges in AI development—taking the next steps beyond local development, model training, and deployment. This session fosters interactive participation, where facilitators and attendees will share common pain points during development, and strategies for solving them. We’ll examine how to unblock fragmented workflows, speed up iteration, and streamline transitions between development environments. Learn how to leverage the potential of LLMs, eliminating friction and accelerating every stage of AI development.