AI Implementation Part 3: AI Technology & Data Foundations - From Concept to Scale

Behind every successful AI initiative lies a strong technical and data foundation. This webinar takes a deep dive into the architecture, infrastructure, and data practices required to move from experimentation to reliable, production-ready AI solutions.

You’ll gain insight into how modern AI systems are structured, including data pipelines, model layers, and integration points with existing systems. We examine different deployment options—such as cloud-based AI, on-premises solutions, and open-source models—and discuss when each approach is most appropriate.

A central theme is the importance of data governance: without high-quality, well-managed data, even the most advanced AI models will fail to deliver value. The session also covers operational topics such as monitoring, logging, traceability, and testing—areas that become critical as AI solutions scale.

By the end, you will have a clearer understanding of the technical decisions and trade-offs that shape successful AI implementations in modern organizations.

Key takeaways

• Importance of datagovernance for AI success
• Typical architecture ofAI solutions
• Cloud vs. on-prem vs.open-source AI models
• Infrastructure,integration, and security design
• Monitoring, logging, andtraceability
• Challenges in scaling AIsolutions

Target audience

• Data engineers
• Software engineers
• IT architects
• Cloud and infrastructurespecialists
• Platform owners
• Cybersecurityprofessionals

 Speaker: Louise Mehl is an external consultant and has many years experience in practical implementation of regulations in the fields of technology, business and requirements.

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