The AI Engineer's Guide to Surviving the EU AI Act • Larysa Visengeriyeva & Barbara Lampl
Posted by goto-con@reddit | programming | View on Reddit | 1 comments
Larysa and Barbara argue that the EU AI Act isn’t just a legal challenge — it’s an engineering one. 🧠⚙️
Building trustworthy AI means tackling data quality, documentation, and governance long before compliance ever comes into play.
👉 Question for you:
What do you think is the hardest part of making AI systems truly sustainable and compliant by design?
🧩 Ensuring data and model quality
📋 Maintaining documentation and metadata
🏗️ Building MLOps processes that scale
🤝 Bridging the gap between legal and engineering teams
Share your thoughts and real-world lessons below — how is your team preparing to survive (and thrive) under the AI Act? 👇
Deeploy_ml@reddit
Bridging the gap between legal and engineering teams is by far the hardest part. Most organizations already have some level of data governance or MLOps, but translating legal requirements into technical controls is where everything stalls.
We see teams doing a lot of interpretation work, taking abstract obligations from the AI Act and figuring out how they map to versioning, approvals, risk assessments, and documentation in practice. That’s where the real engineering challenge lies: making compliance operational instead of theoretical.
At Deeploy we’ve focused on exactly that bridge, turning frameworks like ISO 42001 and the AI Act into controls teams can actually implement and monitor. It’s what makes governance sustainable instead of a box-ticking exercise.
Let me know if you want more details, happy to share some resources.