Great AI ideas are everywhere. Production-ready AI? That's much harder.
Why do so many AI initiatives begin with excitement, deliver promising early results, and then never make it into real-world use?
Join us for the next Svitla Smart Talk, where we'll explore the realities of building AI solutions that create lasting business impact. Drawing on lessons learned from more than 20 real-world AI and data science projects across different industries, this session goes beyond theory to uncover what actually determines whether an AI project succeeds—or stalls.
During the talk, we'll discuss:
- Why promising AI projects fail to reach production
- The most common challenges—from business alignment and communication to data quality, infrastructure, and technical complexity
- Practical approaches that help teams move from experiments to production, including iterative development, MLOps, and planning for long-term adoption
- Real project stories, lessons learned, and actionable recommendations for both technical and business audiences
Whether you're a Data Scientist, ML Engineer, AI Engineer, Software Engineer, Tech Lead, Product Manager, or business leader shaping AI strategy, you'll gain practical insights that can help your next AI initiative deliver measurable results.
If you're looking to build AI products that go beyond impressive demos and create real business value, don't miss this session.
Event details:
Date - 6 August
Time - CDT – 11:00 AM | UA – 7:00 PM | PL – 6:00 PM | MX – 10:00 AM | CR – 10:00 AM | AR – 1:00 PM
Duration - 1.5 hours (60 min. speech + 30 min. Q&A)
Language - English
All other details will be sent after registration.
Registration is free and mandatory.
CEO & Founder @ datarabbit.ai – data/AI custom solutions provider.
Expert, proficient in both preparation/training of ML and DL models, as well as neighboring areas, including but not limited to models’ deployment, scaling, monitoring, and maintenance in cloud environments, MLOps processes and good practices, ML-based solutions productionization, data engineering and management, analytics and BI systems.
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