Navigating the AI Revolution in Software Development
#1about 4 minutes
Understanding AI's dual impact on developer productivity
AI offers significant productivity gains through tools like co-pilots but also raises concerns about job displacement and code quality.
#2about 3 minutes
Navigating the hype and the complex AI tool ecosystem
Developers must cut through the hype by focusing on foundational principles and best practices to select valuable tools from a rapidly growing ecosystem.
#3about 7 minutes
Exploring practical AI use cases and maturity at Zalando
Zalando applies AI across its business, from customer-facing conversational AI to back-office forecasting, demonstrating that maturity is measured by time-to-value.
#4about 9 minutes
Shifting focus from standalone models to complete AI systems
Building robust AI products requires a systems-thinking approach where agentic workflows and components like RAG are integrated, not just focusing on the core model.
#5about 5 minutes
Overcoming the challenges of productionizing AI models
The journey from a working prototype to a production AI system involves a difficult cycle of prompt engineering, MLOps, data labeling, and continuous monitoring.
#6about 6 minutes
Applying software engineering discipline to AI development
The Machine Learning Development Lifecycle (MLDLC) adapts proven SDLC principles to AI, integrating data science and DevOps to manage risk and standardize tooling.
#7about 4 minutes
Ensuring AI reliability with monitoring and data governance
AI systems require specialized monitoring for statistical performance and data drift, alongside strong data governance and adherence to emerging compliance regulations.
#8about 1 minute
Knowing when a problem does not require an AI solution
Practitioners must recognize that AI is a specialized tool and should avoid forcing an AI solution when a simpler, non-AI approach is more effective.
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