Important details:

• You can submit up to 3 talks
• CFP will be open until May 15th
• Talks can be either in Hebrew or English. CFP submissions must be in English

Available speaking slots:

• 30-minute deep dive talks
• 15-minute regular talks
• 5-minute lightning talks

What are we looking for?

This year, we’re especially looking for real-world, production-ready stories, unexpected or boundary-pushing use cases, and innovative data-driven solutions in all the data domains - analytics, engineering, data science, MLOps, DataOps, AI, MCP, and more. If you've built it, scaled it, or learned the hard way—share it with us!

We're into:

• Data in production – beyond the POCs
• Tips, tricks, and lessons from the trenches
• Unusual or bold use cases that challenged the norm
• Real impact, real systems, real data at scale

Data Engineering

• Data Architecture – Modern architectures for scalable, efficient systems
• Data Governance – Managing quality, access, privacy, and compliance
• Real-Time Systems – Low-latency data processing and analytics
• Data Storage – Emerging storage technologies and architectures
• Data Retrieval – Scalable and intelligent retrieval strategies
• AI-Ready Data – Transforming data for AI consumption
• Agentic Frameworks – Using AI agents to deliver customer value

Data Science

• Machine Learning – Classical and deep learning methods and use cases
• Data Preparation & Monitoring – Preprocessing, drift, and visual insights
• Data Modalities – Modeling with time-series, graphs, tabular, text, vision, etc.
• Model Optimization – Hyperparameter tuning, feature selection, imputation
• Scalable Data Science – Large-scale modeling and infrastructure
• ML for Business – Business applications of data science
• MLOps & Deployment – From experimentation to production
• Computer Vision – Production use cases of CV and vLLMs
• LLM Applications – Agents, RAG, chatbots, deployment at scale

Data Analytics & BI

• BI & Analytics Tools – Modern stacks and methodologies
• Data Modeling – Innovative modeling approaches
• Visualization – Advanced and interactive visual techniques
• Self-Service Analytics – Empowering users through accessible data tools
• Online vs Offline – Real-time vs batch data workflows
• Monitoring & Quality – Ensuring trust in data product
• Productizing Analytics – Turning insights into products
• Data Culture – Building data-first teams and mindsets
• Semantic Layers – Structured access to complex data
• AI in BI – Embedded AI and automation in analytics
• Tools & Bots – Automations that scale decision-making
• Inspiring Analyses – Strategic, high-impact insights that moved the needle

General Topics

• Scaling Data Teams – People, processes, and pitfalls
• Cross-Team Collaboration – Breaking silos in data orgs
• Hiring & Leadership – Growing and managing data talent
• Model vs Service Innovation – Balancing tech and value
• Real-Time Data Ops – Challenges and strategies
• Data in Production – Lessons learned, do’s and don’ts