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From zero to AI-driven in less than a month




Habana DL1
Intel Developer Cloud


Recommender Systems

TOOLS is perfect for teams that want to start AI with the best MLOps practices. From the moment we started using, it took only 1 month to deliver AI results because it is intuitive, centralized and simple to use. With we were able to focus more time experimenting on the model itself, rather than learning the platform and learning how to operationalize our model
Brice Macias
Software Engineer Shotgun

About Shotgun

Shotgun is a global live entertainment solution on a mission to change the way party planners and party lovers interact. Shotgun is a ticketing SaaS that allows producers to structure their event projects with a set of tools to maximize the sales of tickets and increase their audience. As to the public, Shotgun deploys different platforms that respond to the new consumption habits of live entertainment turning going-out people into community.


Shotgun is transforming the live entertainment industry with state of the art technology. With over 2 million monthly visitors, Shotgun offers a great community and event discovery experience for event goers, and an exceptional experience for event planners that increases sales, and expands reach. Shotgun’s suite of technology tools enable a seamless experience for all of their customers. Shotgun is always seeking ways to leverage the latest technologies to remain competitive and deliver the best platform out there for live entertainment. The company sought out to quickly bring AI-powered enhancements into their solutions.


Starting from zero AI to AI-powered

In order to begin integrating AI into their solution, Shotgun required an efficient AI platform that would enable their small team of engineers to deliver AI quickly. With limited experience in AI, it was critical that the platform chosen be flexible, and intuitive so they could deliver AI faster into their applications. In their MLOps vendor search, they found most platforms to be fragmented and constrained to only using the compatible computing vendors or tools.

Shotgun’s first AI project was to implement a recommender system that would take user history from various sources to offer advanced recommendations for events based on user’s event and music tastes. Additionally the system required quick recommendations with a cache solution to give users real-time and relevant recommendations. As Shotgun embarked on their AI journey, they needed a platform that was flexible and scalable, so that their team could quickly build and support new AI innovations as they grew.


Delivering custom AI solutions intuitively and seamlessly

After a thorough evaluation of various cloud vendor platforms, Shotgun found to be a perfect fit for their needs. offered the most comprehensive and flexible platform on the market. The engineers at Shotgun felt that was intuitive and simple enough for their immediate needs in the early stages of their AI journey, but could also deliver on future needs as their AI maturity advances and as their team grows. What Shotgun found was that the platform had a good balance enabling the AI team to build complex and custom AI, while also simplifying MLOps tasks so they could focus on innovation. Shotgun was able to use the simple pre-made recommender system AI Blueprint and customize it with their own code and data. takes care of all of the engineering tasks like scheduling, and pipeline creation, and retraining that otherwise would have required advanced engineering knowledge. As an added benefit, Shotgun is able to leverage AI optimized resources easily via native integration to the Intel Developer Cloud.

  • Centralization of different features in one single dashboard
  • Simplified and automated ML pipeline development
  • Easy to use scheduler 
  • Intuitive UX and automated MLOps for accelerated onboarding


From setup to AI results in one month

In just one month, Shotgun was able to onboard to and build and optimize their recommender system for production. Shotgun accelerated their time to value 5x. What would have taken Shotgun at least 6 months to deliver manually was done in 1 month using by reducing the time needed to research and validate MLOps best practices. With, Shotgun was able to easily adopt MLOps with confidence, out of the box. The solution enabled them to bypass the technical debt of operationalizing models, and focus on developing new innovations that will make them more competitive in the market.

  • Accelerated time to AI value from 6 months to 1 month
  • Simplification of scheduler reduced need for MLOps engineer 
  • Reduced cost of AI by enabling cost effective computing and need to hire AI talent
  • Optimized performance with ability to leverage Intel Developer Cloud purpose built resources
  • Spend 5x more time on building solutions instead of engineering
  • Accelerate training time with optimized hardware