Running Generative AI in Production –
As your data projects evolve? you will face new challenges. For new technology like generative AI? some challenges may just be variations on traditional IT projects like considering availability or distributed What Issues Will computing deployment problems. However? generative AI projects are also going through what Donald Rumsfeld once called the “unknown unknowns” phase? where we are discovering potential issues that we did not consider before.
To solve these issues? we can apply some of the best practices for IT and data management. However? we also must look for ways to overcome the problems faced by generative AI. This may also feed back into our data preparation approaches.
Where Data Management Issues Exist at Scale
We’ve all heard the old joke about code working What oman whatsapp number data Issues Will on a developer laptop? and so that code is put into production. After all? the logic goes? if it works on one machine? it should work on larger instances. However? while testing and implementing proof of concept instances can yield a certain level of success? these results are not representative of operating at scale when supporting hundreds of thousands? if not millions? of requests.
Supporting your infrastructure over multiple locations delivers resiliency and availability for your application or service. By planning your approach to continuity? you can survive failure in one or more of your have data deletion procedures in place components and still function effectively. As generative AI applications move into production? you will have to think about availability and resiliency for the data that this service uses.
Generative AI applications rely on vector data
As we scale up generative AI applications? we alb directory increase the amount of data stored as vectors as well. Data sources like product catalogs? customer records? and historical data sets can all be turned into vector data that can be used with generative AI through retrieval augmented generation (RAG). RAG helps improve the quality of the responses that AI systems provide by leveraging a company’s data alongside any other relevant data sets.