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Dec 12, 2024

Navigating the AI Revolution in Software Development

Are you treating every software challenge like an AI nail? Move past the hype and learn to engineer resilient, production-ready machine learning systems.

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#1 about 6 min

Motivations for adopting AI to enhance developer productivity

Software engineers must critically assess how AI code generators affect software quality despite extreme industry hype.

#2 about 4 min

Navigating the expanding ecosystem of new machine learning tools

Developers face an overwhelming influx of unproven frameworks requiring strict first principles to properly navigate.

#3 about 4 min

Applying predictive and generative models in ecommerce environments

Organizations leverage multimodal techniques to automate large-scale backend optimization and personalize retail shopping experiences.

#4 about 5 min

Evaluating technical maturity and balancing organizational time to value

Teams capture competitive advantages by balancing highly reliable foundational methodologies with novel transformer models.

#5 about 3 min

Transitioning from standalone experimental models to robust production systems

Scaling machine learning features requires treating them as interconnected software architectures rather than isolated algorithms.

#6 about 5 min

Building agentic workflows using prompt engineering and language models

Orchestrating autonomous interactions between backend microservices and language models unlocks advanced capabilities for standard consumer applications.

#7 about 2 min

Implementing safety guardrails and content filters in generative platforms

Protecting user data privacy necessitates architectural middleware configurations that intercept restricted query patterns.

#8 about 2 min

Prioritizing human impact and professional responsibility in AI engineering

The missing standardization of engineering practices demands that practitioners establish ethical constraints protecting critical societal infrastructure.

#9 about 4 min

Overcoming technical constraints in production machine learning operations

Deploying experimental prototypes into live environments introduces continuous operational bottlenecks regarding validation and concept staleness.

#10 about 5 min

Adapting traditional software lifecycles to accommodate machine learning risks

Project deployment structures must shift from rigid delivery pipelines toward iterative testing cycles managing statistical uncertainties.

#11 about 2 min

Mitigating security vulnerabilities across the machine learning operational lifecycle

Hardening data processing pipelines protects external dependencies against arbitrary code execution and supply chain manipulation.

#12 about 2 min

Monitoring statistical performance and model data drift in production

Maintaining long-term application usability relies on dynamically analyzing statistical metric variances rather than basic binary health checks.

#13 about 1 min

Prioritizing foundational data engineering to build highly accurate models

Sophisticated classification algorithms inevitably generate flawed insights unless administrators aggressively curate structural integrity within underlying training inputs.

#14 about 2 min

Aligning open source frameworks with emerging AI compliance regulations

Enterprise engineering divisions must tether their core foundational decisions to complex governmental safety mandates regarding algorithmic transparency.

#15 about 2 min

Choosing standard software solutions over unnecessary artificial intelligence tooling

Development teams squander financial resources when unnecessarily deploying probabilistic language models toward challenges easily resolved through simple deterministic pathways.

Matching moments

2:08 min

Essential engineering roles in the generative AI space

Mary Grygleski Mary Grygleski · LIVE

1:25 min

Expanding AI across the product development lifecycle

Daniel Geisel Daniel Geisel +1 · World Congress 2026 Europe

4:19 min

Introduction to DevOps for AI and MLOps

Aarno Aukia · LIVE

4:15 min

Introduction to artificial intelligence driven development

Natalie Pistunovich · LIVE

4:01 min

Transitioning artificial intelligence into operational business environments

Stefan Donsa Stefan Donsa +1 · LIVE

1:57 min

Evolution of artificial intelligence in software engineering

Chris Heilmann · World Congress 2024

Upcoming sessions on this topic

Open session

World Congress 2026 North America

September 25, 2026 · 14:10–14:40

Stage 1

The Broken Rung: How AI is Rebuilding Software Development from the Ground Up

Tomislav Tipurić

Chief Technology Officer, Nephos

Tomislav Tipurić
Open session

World Congress 2026 North America

September 24, 2026 · 16:50–17:20

Mainstage

Building AI Products vs. Building With AI

Aparna Dhinakaran, Rukmini Reddy, Tamar Bercovici

Aparna Dhinakaran
Rukmini Reddy
Tamar Bercovici
Open session

World Congress 2026 North America

September 24, 2026 · 11:10–11:15

Outdoor Stage

Architecting the 100X SDLC: Building Production Trust into AI-Assisted Delivery

Ranjan Parthasarathy

Founder, CPTO/CEO at AXIOMSTUDIO.AI

Ranjan Parthasarathy
Open session

World Congress 2026 North America

September 25, 2026 · 12:55–13:25

Outdoor Stage

Building the AI Era Through Developer Communities: A Competitive Advantage for Engineers & Teams

John Komarnicki

National Executive Advisor at Code & Coffee | Engineering & AI Community Leader

John Komarnicki
Open session

World Congress 2026 North America

September 23, 2026 · 10:00–17:00

Stage 11

Building Stuff with GenAI - The Open Minded Workshop beyond OpenAI

Andreas Erben

CTO for Applied AI and Metaverse at daenet

Andreas Erben
Open session

World Congress 2026 North America

September 25, 2026 · 11:40–12:10

Stage 2

Reinventing Testing Practices in the AI Era

Eric Deandrea

Java Champion & Senior Principal Software Engineer, IBM

Eric Deandrea

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