Important details:

• You can submit multiple talks
• CFP will be open until May 18th
• 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?

Below are suggested subjects (but topics are not limited to these suggestions)

Data Engineering

• Data Architecture - High-level data architecture and/or deep dive into various related technologies (e.g. data mesh design, data lake implementations, data platform components, multi-site/cloud data strategy).
• Data Governance - Technologies & concepts including data catalogs, data access control tools, data quality, and data privacy.
• Real-Time Analytics Databases - Architectures, performance optimization, scalability solutions, and case studies demonstrating real-time data processing and analytics capabilities.
• Advanced Data Storage Technologies - Innovations in data storage, including but not limited to, distributed databases, blockchain-based storage solutions, quantum data storage, and non-volatile memory technologies.
• Cutting-Edge Data Retrieval Technologies - Techniques and algorithms for efficient data retrieval, including indexing strategies, machine learning-driven search optimizations, and graph databases.

Data Science

• Deep Learning and Classical Machine Learning - Machine Learning Applications, Learning in knowledge-intensive systems, Learning Methods and analysis, Learning Problems, Deep Learning.
• Pre-Processing Techniques, Visualization - Statistics for intelligent data analysis, Outlier analysis, data visualization, data drifting, and monitoring.
• Data Types and Data Modeling - Time-series, Tabular data, Graphs, Text, Computer Vision, Web Mining, IOT, Voice and Multimedia.
• Models optimization - Hyperparameters Tuning, Feature Selection, Imputation.
• Data Science at scale - Large-scale data mining and analysis, Consistent Data Model.
• Talking Business - Data Science for business applications.
• MLOps and fastest path to production - ML experiment management - best practices and lessons learned, managing multiple models in production, moving from exploration to production, and API culture in DS teams.
• Trends and future of Data Science
• Retrieval-Augmented Generation (RAG) in Data Science - Insights on integrating RAG with machine learning and data analytics to enhance model performance and decision-making.

Data Analytics and BI

• Business Intelligence (BI) Tools and Methodologies - Explore the latest tools, techniques, and best practices in BI, including data visualization and advanced analytics.
• Data Warehouse (DWH) Architecture - Discuss the design, implementation, and optimization of DWH architectures, covering data modeling and integration strategies.
• Data Modeling - Present innovative approaches to data modeling, including conceptual, logical, and physical models.
• Data Visualization - Showcase cutting-edge visualization techniques and tools, including interactive visualizations and visual analytics.
• Self-Service Analytics - Explore self-service analytics tools and methodologies, focusing on empowering users and data democratization.
• Online vs. Offline Processing - Compare and contrast online and offline data processing techniques, covering real-time and batch processing.
• Data Monitoring -Discuss strategies and tools for monitoring data quality, governance, and security, including real-time monitoring and anomaly detection.
• Productizing BI - Explore the process of productizing BI solutions, including packaging for commercial use and best practices for product development.
• Data Analytics - Discuss trends and developments in data analytics.
• Data-Driven Culture - Examine strategies for fostering a data-driven culture within organizations, including promoting data literacy and building data-driven teams.
• Semantic Layers for Data Analytics - Explore the use of semantic layers in data analytics.

Suggested general topics

• Managing data teams for organizational scale
• The boundaries and collaboration between different departments
• Hiring, managerial, and data talent challenges
• Model innovation vs/and service innovation
• Working with data in real time - issues and solutions

And any other deep-dive topics related to Data Science, Big Data, Data Engineering, BI, and Data Analytics domains.