InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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If You Can’t Test It, Don’t Deploy It: The New Rule of AI Development?
Magdalena Picariello reframes how we think about AI, moving the conversation from algorithms and metrics to business impact and outcomes. She champions evaluation systems that don't just measure accuracy but also demonstrate real-world business value, and advocates for iterative development with continuous feedback to build optimal applications.
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Elena Samuylova on Large Language Model (LLM)-Based Application Evaluation and LLM as a Judge
In this podcast, InfoQ spoke with Elena Samuylova from Evidently AI, on best practices in evaluating Large Language Model (LLM)-based applications. She also discussed the tools for evaluating, testing and monitoring applications powered by AI technologies.
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AI, ML, and Data Engineering InfoQ Trends Report 2025
In this episode, members of the InfoQ editorial staff and friends of InfoQ discuss the current trends in the domain of AI, ML and Data Engineering. One of the regular features of InfoQ are the trends reports, which each focus on a different aspect of software development. These reports provide the InfoQ readers and listeners with a high-level overview of the topics to pay attention to this year.
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Why Rust Will Help You Deliver Better Low-latency Systems and Happier Developers
Andrew Lamb, a veteran of database engine development, shares his thoughts on why Rust is the right tool for developing low-latency systems, not only from the perspective of the code’s performance, but also looking at productivity and developer joy. He discusses the overall experience of adopting Rust after a decade of programming in C/C++.
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Mandy Gu on Generative AI (GenAI) Implementation, User Profiles and Adoption of LLMs
In this podcast, Mandy Gu from Wealthsimple discusses how to establish AI programs in organizations and implement Generative AI (GenAI) initiatives, and the relationship between user profiles and adoption of LLMs.