Welcome to the Generative AI World Cup by Databricks in collaboration with Amazon Web Services (AWS), where data scientists and AI/ML engineers team up to tackle real-world challenges with cutting-edge GenAI apps in their specific industry. Databricks will present industry-specific problems for participants to solve. Can you craft the most high-quality and innovative GenAI app that stands out in this competition, the one you can brag about? Build, iterate, refine your solutions, and transform your industry. Don’t miss your chance to shine in this thrilling competition and compete for a pool of over $100,000 in cash prizes!
Requirements
What to Build
Entrants must create a working Generative AI software application that fits into one of the Themes and uses Databricks. Projects should be in the following categories: 1) Healthcare & Life Science; 2) Financial Services; Retail & Consumer Goods; 3) Communications, Media & Entertainment; or 4) Manufacturing & Energy
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Retail and Consumer Goods (RCG) |
Merge customer, inventory and POS data to optimize the customer experience In-store or online agents and employees at retailers can get a single view of the customer and inventory with recommendations to provide better real-time customer service, support, and guidance in-store or online. Empowering human agents with generative AI-enabled tools like chatbots or portals can provide a better customer experience, increase the share of wallet and increase customer lifetime value. |
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Financial Services |
Analyze public and private data to make better investment decisions Asset managers can leverage news analytics such as GDELT to better understand trends and geopolitical events globally that may affect their investment portfolios and decisions. |
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Healthcare and Life Sciences |
Increase the efficiency of healthcare operations The estimated potential savings from waste reduction in healthcare ranges anywhere from $191 billion to $286 billion. Medical coding and billing are highly manual, medical transcription costs are expensive, clinical documentation is burdensome, and with manual processing, there is a risk of medication errors. Help us address this inefficiency in healthcare that has a material impact on staff productivity, cost reduction and even on patients. |
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Manufacturing and Energy |
Improve productivity of field service agents Field service engineers are often challenged with accessing tons of documents that are intertwined and are long and complicated. Having an LLM or context-aware Q&A bot will significantly reduce the time required to diagnose the problem and will boost efficiencies. The challenge that manufacturers face is how to build and incorporate data from proprietary documents and internal knowledge bases into LLMs. |
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Communications, Media and Entertainment |
Maximize audience experience Generative AI chatbots enable customers to better self-serve, reducing the burden on human customer support agents. A similar generative AI chatbot can be internally facing, empowering human support agents with enhanced customer insights and the ability to query using natural language and increasing the throughput and efficacy of agents solving customer support queries. |
What to Submit
- a NEWLY built Databricks Project that fits into one of the above themes
- Access must be provided to a working Project for judging and testing by providing a link to a website, functioning demo, or a test build
- Text description - Please include the name of each teammate, your company email(s), country, job title, and what industry you are applying your submission for
- Demonstration video (around 5 minutes)
See Full Rules for more details.
Prizes
[First Place] Best Healthcare & Life Science Generative AI App
- $10,000
- $5,000 Databricks Credit
- Tickets to Data + AI Summit (up to 4 per team)
- Featured winner in blog and summit
[First Place] Best Financial Services Generative AI App
- $10,000
- $5,000 Databricks Credit
- Tickets to Data + AI Summit (up to 4 per team)
- Featured winner in blog and summit
[First Place] Best Retail & Consumer Goods Generative AI App
- $10,000
- $5,000 Databricks Credit
- Tickets to Data + AI Summit (up to 4 per team)
- Featured winner in blog and summit
[First Place] Best Communications, Media, & Entertainment Generative AI App
- $10,000
- $5,000 Databricks Credit
- Tickets to Data + AI Summit (up to 4 per team)
- Featured winner in blog and summit
[First Place] Best Manufacturing & Energy Generative AI App
- $10,000
- $5,000 Databricks Credit
- Tickets to Data + AI Summit (up to 4 per team)
- Featured winner in blog and summit
[2nd Place] Best Healthcare & Life Science Generative AI App
- $5,000
- $2,500 Databricks Credit
- Tickets to Data + AI Summit (up to 4 per team)
[2nd Place] Best Financial Services Generative AI App
- $5,000
- $2,500 Databricks Credit
- Tickets to Data + AI Summit (up to 4 per team)
[2nd Place] Best Retail & Consumer Goods Generative AI App
- $5,000
- $2,500 Databricks Credit
- Tickets to Data + AI Summit (up to 4 per team)
[2nd Place] Best Communications, Media, & Entertainment Generative AI App
- $5,000
- $2,500 Databricks Credit
- Tickets to Data + AI Summit (up to 4 per team)
[2nd Place] Best Manufacturing & Energy Generative AI App
- $5,000
- $2,500 Databricks Credit
- Tickets to Data + AI Summit (up to 4 per team)
Devpost Achievements
Submitting to this hackathon could earn you:
Judges
Sean Owen
Staff Research Scientist, Databricks
Daniel Liden
Sr. Developer Advocate
Nick Karpov
Staff Developer Advocate, Databricks
Sam Raymond
Staff Data Scientist, Databricks
Corey Abshire
Sr. Specialist Solutions Architect, Databricks
Cheng Yin Eng
Staff Data Scientist, Databricks
Saurabh Shukla
Sr. Specialist Solutions Architect
Brian Law
Sr. Specialist Solutions Architect, Databricks
Jackie Zhang
Senior Product Marketing Manager, Databricks
Judging Criteria
-
Creativity
Is this a new and original idea, or has this been done before? -
Business Applicability
How well does this solve a real business problem for your industry? -
Data Relevance
How have you combined relevant and interesting datasets and tools? -
Thoroughness
Is your application easy for the end user to understand? Does it provide relevant and insightful information? -
Well-architected
Can your application/RAG model scale well at a linear cost? Can it accommodate additional features without rewriting most of the code base?
Questions? Email the hackathon manager
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