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NEOZO AI Meetup × ClickHouse: How Modern Data Platforms Power AI

Event artwork for NEOZO AI Meetup × ClickHouse: How Modern Data Platforms Power AI

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NEOZO Cloud Co. Ltd.
About this event

What to expect

Explore how modern data platforms support production AI at this NEOZO Cloud × ClickHouse meetup in Bangkok on 1 September 2026. The evening connects the data infrastructure behind agents, copilots and LLM features with practical lessons from AI-ready architecture and FinTech machine learning.

Taking place from 6:00 PM to 9:00 PM ICT at NEOZO Cloud Co. Ltd. in CTI Tower, the event combines two focused technical talks with dedicated time to meet other members of Bangkok’s AI and data community. Topics span real-time ingestion, vector workloads, LLM application observability, feature pipelines and the high-value data,including traces, embeddings and evaluations,that determines whether AI systems work effectively in production.

Talks and speakers

The AI-Ready Data Lakehouse: Scaling Intelligence While Optimizing Costs

Maximilian (Max) Simmoteit of NEOZO will examine modern data architectures that help organisations scale analytics, machine learning and production AI while managing costs. Max has spent the past four years specialising in data engineering and solution architecture. His background also includes mathematics, computer science, C++ optimisation software and full-stack Java development.

ClickHouse for Feature Engineering in FinTech

Alua Magavyanova, Fraud Data Scientist and Risk Data Analyst at Yusr Fintech in Dubai, will share a practitioner’s view of using ClickHouse as the foundation for production machine-learning feature engineering. Drawing on more than five years of FinTech experience, she will cover practical examples and lessons from building thousands of features for fraud detection, credit risk and portfolio analytics.

The session will explore feature generation across:

  • Transactional behaviour and session analytics
  • Device intelligence and geolocation
  • Graph-based relationships and deduplication
  • Internal credit history and other large-scale analytical datasets
  • Rapid experimentation and production-scale analytical workflows

Rather than concentrating only on infrastructure, the talk will include challenges encountered while supporting fraud and portfolio risk models with SQL, Python and ClickHouse.

Format and audience…

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