Important details:

• You can submit up to 3 talks
• CFP will be open until July 12th
• Talks can be either in Hebrew or English. CFP submissions must be in English
• Sales-oriented talks will not be accepted. We are NOT allowing sales pitches, and all submissions must focus on knowledge sharing and technical insights.
• We highly encourage speakers to submit new and original content. Please avoid submitting talks that already have recordings available on YouTube or other public platforms. If your talk is accepted, we ask that you do not present the same talk at any other conference, meetup, or public event before hayaData takes place.

Available speaking slots:

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

What are we looking for?

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, data products, MLOps, DataOps, AI, and more. As the data world evolves, we're also excited to hear how emerging technologies, AI-powered tools, and new methodologies are transforming the way data professionals work, build, and deliver value.

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
• Integrating AI Methods into Traditional Data Practices

Some examples:

Data Engineering

• Modern Data Platforms & Architectures – Lakehouse ecosystems, interoperability, platform engineering, and scalable distributed systems
• Streaming, Real-Time & Operational Data Systems – Event-driven architectures, low-latency processing, and continuously updated data products
• Metadata, Semantics & Context Engineering – Active metadata, lineage, semantic layers, knowledge graphs, contextual retrieval, and intelligent data discovery
• Data Platforms for AI & Intelligent Workloads – RAG pipelines, vector retrieval, semantic context systems, evaluation workflows, and operational infrastructure for AI-powered applications
• Governance, Security & Data Reliability – Data contracts, observability, privacy, compliance, policy enforcement, and trustworthy data systems at scale
• Developer Experience & Data Platform Operations – Self-service infrastructure, orchestration, CI/CD, automation, governance workflows, and scalable operational tooling for data teams
• Performance, Scalability & FinOps – Cost-efficient architectures, workload optimization, storage and compute efficiency, and operating large-scale data platforms sustainably

Data Science

• Hybrid AI & Classic ML - Combining new and innovative AI workflows in classic ML/ DL realms.
• Agentic Data Science - AI agents for deep research, experimentation execution, and data workflows.
• LLMs for Data Science - Fine-tuning, RAG, embeddings, prompt engineering, and multi-agent systems with an innovative mindset.
• AI-Assisted Development - Using AI to accelerate development, agentic workflows that move the needle on your day-to-day.
• AI Evaluation & Trust - Measuring, validating, monitoring, and governing AI and ML systems.
• Production AI & MLOps - Deploying, scaling, and operating ML and AI solutions.
• Data Foundations - Data quality, labeling, synthetic data, and multimodal data - everything supporting your ML applications.
• Foundation Models & AI Platforms - Leveraging foundation models at scale, building AI-native data platforms, serving infrastructure, embeddings, vector systems, and enterprise AI architectures.

Data Analytics & BI

• Data Platforms & Architecture – Designing scalable foundations for analytics, self-service, AI, and data products
• AI-Enabled Analytics – Applying AI to enhance analytical workflows, insight generation, and decision-making
• Analytics Platforms & Tooling – Modern stacks, workflows, and methodologies in the AI era
• Data Modeling – Innovative modeling approaches for analytics, metrics, and AI consumption
• Self-Service Analytics – Powering business users and AI agents with accessible, trusted data
• Semantic Layers & Metrics Management – Building shared business definitions and consistent data access
• Data Quality & Observability – Creating trust in analytics through monitoring, testing, and governance
• Analytics Automation – Bots, workflows, agents, and systems that scale analytical operations
• Productizing Analytics – Turning insights, models, and analytical capabilities into reusable products
• Visualization & Storytelling – Effective, interactive, and impactful data communication
• Analytics Governance – Managing definitions, lineage, ownership, and consistency across organizations
• Data Culture – Building data-driven organizations, communities, and decision-making frameworks
• Real-Time Analytics – Operational analytics, streaming insights, and hybrid real-time/batch architectures
• Inspiring Analyses – Strategic investigations and analytical projects that created measurable business impact

Data Products

• Data Products in Production – Building, launching, and scaling data products that users actually adopt
• Productizing Data & AI – Turning models, pipelines, dashboards, and insights into usable products
• Data Product Strategy – Prioritization, roadmaps, discovery, and measuring product impact
• User-Centered Data Experiences – Designing data tools, analytics, and AI capabilities around real user needs
• Data Product Management – Roles, ownership models, rituals, and collaboration between data, product, engineering, and business teams

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
• AI Adoption in Data teams - challenges and lessons learned
• Shifting the organizational mindset - from data for analysis to data for production