Data + AI Summit 2024 Recap: Focus on Democratization and Innovation

Nidhi Inamdar|July 4, 2024|12 Minute read|
/ / Data + AI Summit 2024 Recap: Focus on Democratization and Innovation




The Data + AI Summit 2024 kicked off with a high-energy keynote by Ali Ghodsi, co-founder and CEO of Databricks. Ghodsi expressed his excitement about the event, highlighting it as his favorite week of the year. This global event is the largest gathering of data and AI professionals in the world, with over 60,000 participants worldwide and 16,000 attendees at the venue.  

Impressive Statistics and Global Reach 

Ghodsi began by sharing impressive statistics about the summit: 

  • Over 60,000 global participants. 

  • 16,000 in-person attendees. 

  • Representation from 140 countries. 

  • 600 training sessions on data and AI. 

  • 200 teams presenting their work. 

The summit also featured 143 exhibitors showcasing a variety of products and innovations. This massive participation underscores the global interest and investment in data and AI. 

Celebrating Open-Source Contributions 

Databricks has significantly contributed to the open-source community. The Spark project, Delta Lake, and MLflow have all seen over a billion downloads annually. Databricks employees have contributed 12 million lines of code to open-source projects, a testament to their commitment to the open-source ecosystem.

The Lakehouse Platform 

Databricks aims to address these challenges through its Lakehouse platform. The CEO outlined their vision: 

  • Ownership of Data: Organizations should own their data, storing it in cheap, scalable cloud data lakes rather than relying on proprietary vendors. 

  • Open Formats: Utilizing open-source formats like Delta Lake and Apache Iceberg to ensure compatibility and avoid vendor lock-in. 

  • Unified Governance: The Unity Catalog, an open-sourced governance layer, provides comprehensive data management, access control, and security across the entire data estate. 

Key Challenges in Data and AI 

Ghodsi addressed three major challenges that organizations face in the data and AI landscape: 

  1. Adoption of Generative AI (GenAI): 

  • Organizations are eager to implement GenAI but struggle with getting models to perform well on their specific data and use cases. 

  • A survey revealed that 85% of GenAI use cases are not yet in production. 

  1. Security and Privacy: 

  • There is intense pressure to ensure data security and privacy amidst increasing cyber threats and potential AI regulation. 

  • Organizations need to secure their entire data estate, including AI models, notebooks, and dashboards. 

  1. Fragmentation of the Data Estate: 

  • Many organizations face fragmentation with multiple data warehouses and platforms, leading to complexity, high costs, and vendor lock-in. 

Data Intelligence: The Future of Data and AI 

Databricks is focused on combining the Lakehouse platform with generative AI to create "Data Intelligence." This approach involves: 

  • Training generative AI models on each customer's data in isolation. 

  • Democratizing data access and AI capabilities across the organization. 

The acquisition of Mosaic AI, a platform for training custom AI models, is a crucial step in this direction. By integrating Mosaic AI with the Lakehouse platform, Databricks aims to empower organizations to leverage AI more effectively and innovate faster. 

Highlights from Keynote Speakers 

The lineup of speakers included notable figures such as the DuckDB Creator, Professor Fei-Fei Li, Jensen Huang, and representatives from General Motors (GM), among others. Each speaker brought unique insights and groundbreaking advancements to the forefront. 

General Motors: Leveraging Data and AI 

GM showcased how they leverage data and AI to enhance operational efficiency, focusing on predictive maintenance, supply chain optimization, and improving customer experience. This presentation highlighted the practical applications of data and AI in the automotive industry, demonstrating significant advancements and future potential. 
General Motors (GM) underwent a significant data transformation journey to stay competitive in the AI and ML driven auto industry. Previously, siloed data and fragmented governance structures made data management inefficient. To address this, GM embraced the cloud, built the Insight Factory on Databricks, and invested in AI/ML tools. These efforts streamlined data management, enabled rapid generation of insights, and improved data governance. An example of success includes using the platform to enhance customer safety by integrating data sources and expediting the generation of actionable insights. As GM continues to innovate, they look to recruit talent passionate about data and AI/ML to help them achieve their vision of zero crashes, zero emissions, and zero congestion. 

Mosaic AI: Building and Deploying Custom AI Models 

Patrick Wendell talks about Mosaic AI. It provides serverless GPUs for building and deploying custom AI models. The platform focuses on preparing data for AI, training models using advanced techniques, deploying in production, and ensuring governance and security. This comprehensive approach allows organizations to leverage AI effectively and securely. 

mosaic AI

One of the most important features that was launched was the Mosaic AI Gateway, which offers a smooth interface via which organizations can integrate and make use of Mosaic AI's capabilities, improving their capacity to effectively develop and implement custom AI models using serverless GPUs. 

Mosaic AI Gateway,

Kasey Uhlenhuth provided a live demonstration of the product’s capabilities. 

Key Takeaways from Mosaic AI

Jackie Brosamer, Head of Platform Engineering for AI, Data, and Analytics at Block described how her group created and executed generative AI solutions using the Mosaic AI platform. She talked about the difficulties that Block has because of its dispersed business units and variety of use cases, highlighting the necessity for an adaptable data platform that can handle large amounts of both structured and unstructured data. 

Spatial Intelligence 

At the session, Fei-Fei Li, a professor at Stanford University, discussed the development and recent advances in artificial intelligence, with a focus on computer vision and spatial intelligence. She outlined the development of AI from simple item recognition to intricate jobs like text-to-image generation and 3D environment comprehension. She highlighted the development of ethical AI, showcasing possible uses in healthcare and other fields, and envisioned AI as a partner in improving human productivity and quality of life. To ensure that AI benefits society ethically, her discussion emphasized the significance of merging technology advancement with ethical issues. 

The transformative power of A.I and Data Intelligence 

The founder and CEO of NVIDIA, Jensen Huang, is an enthusiastic speaker at industry events and forums on the transformative power of AI and data intelligence. He highlights how important data is to businesses as the new currency, comparing it to unexplored gold mines that, when combined with AI, may produce significant business insights and efficiencies. Huang emphasizes the role that AI plays in fostering innovation in a variety of industries and argues that its integration will improve operational procedures while also radically changing corporate strategies. 

Huang also supports the democratization of AI by pointing to open-source models' capacity to promote innovation in the AI community and enable wider access. He draws attention to NVIDIA's efforts to advance AI capabilities, including their integration of open-source models into frameworks like LLaMA 3 and their acceleration of data processing capabilities. Huang's forward-thinking perspective goes beyond technology development to tackle sustainability issues, suggesting that artificial intelligence (AI) has the power to optimize energy use and promote environmental efficiency in a range of sectors. 

Essentially, Jensen Huang's talks highlight his pioneering role in developing AI technologies and molding their revolutionary effects on international sectors, stressing innovation and sustainability as the cornerstones of NVIDIA's objective and industry impact. 

Importance of Data Warehousing

Co-founder of Databricks Reynold Xin emphasizes the significant expansion and influence of Databricks SQL in the data warehousing industry. He emphasizes how 7,000 clients worldwide—including well-known brands like Shell, AT&T, and Adobe—have quickly come to rely on Databricks SQL for their data warehousing requirements. According to Xin, the idea of the "lake house" evolved to combine the benefits of both traditional data warehouses and data lakes, doing away with the need for complicated governance and data silos. He highlights Databricks' dedication to improving platform performance, pointing to notable developments like the more than 70-fold improvement in warehouse acquisition time, which was reduced from 370 to less than 5 seconds. Xin highlights Databricks' commitment to ensuring that data warehousing is effective and accessible for all users, highlighting improvements in usability and AI-driven capabilities that empower both business users and analysts. 

Importance of Data Warehousing

 SQL Capabilities of Databricks 

Pearl Ubaru showed off Databricks SQL's capabilities at the event, showcasing how AI queries, materialized views, and predictions can all be easily integrated to improve user experience. Pearl demonstrated how easy it is to build predictive models directly in SQL, generate forecasts, and easily create interactive visualizations with a live demo. She highlighted the platform's usability for users of all skill levels, emphasizing how it can streamline data workflows across different BI products, including as Power BI, and how it can leverage AI-powered insights for thorough data analysis and decision-making.

Democratizing Data and Analytics 

Ken Wong gave an address that centered on the shortcomings of conventional business intelligence tools and the revolutionary possibilities of artificial intelligence. He underlined that because every organization has different data issues and domain-specific semantics, it is not enough to merely add generic language models (LLMs) to BI tools to fully realize the potential of AI. As a starting point, Ken presented Databricks AIBI (AI Business Intelligence), which integrates AI into business intelligence (BI) technologies to facilitate automated insights generation, natural language querying, and continuous learning customized for business scenarios. Using conversational AI and user-friendly dashboards, this method seeks to democratize data and analytics. It also promises to enhance and evolve. 

AI Business Intelligence

During her talk, Miranda Lun highlighted the features of Databricks AIBI (AI Business Intelligence), emphasizing the latter's conversational AI capabilities via Genie in addition to conventional dashboarding. She started by showcasing how simple it is to use Databricks' point-and-click interface or SQL queries to create visualizations like bar charts directly from CRM data. Miranda emphasized how the platform can manage intricate data cleansing and context inference, guaranteeing precise insights. 

Miranda demonstrated how users can ask natural language inquiries like "How's my pipeline?" and receive dynamic visualizations and insights in return by switching to AIBI's conversational AI, Genie. She highlighted Genie's capacity to pick up on company-specific semantics instantly, defining concepts like "churn" without requiring human modifications to semantic layers.  

In her closing remarks, Miranda demonstrated how AIBI enhances data democratization and operational efficiency within organizations by enabling people to do complicated analytics and explore deeper insights using natural language interactions. 


To sum up, Databricks is at the forefront of transforming the intersection of data and AI to address the most critical issues facing the planet. Databricks' innovative Data Intelligence Platform, which combines AI with total data control, enables businesses to overcome obstacles and spur never-before-seen levels of innovation. This platform aims to democratize AI's transformative potential and enable proactive, cooperative solutions to global problems including healthcare advancements, environmental sustainability, and societal justice. It is not only about using data. The goal of Databricks transcends traditional limitations, simplifying complexity and transforming reactive strategies into proactive actions. The future of data intelligence with Databricks is about what can be discovered and accomplished, not just what is known. Working together, we can forge new paths and create a better tomorrow for all. 

Also read, Lucent Innovation: Exhibiting at TECHEX (USA)

Nidhi Inamdar

Sr Content Writer

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