Generative AI model development, including tasks such as training, fine-tuning, and inferencing, is often performed in cloud environments thanks to the ease of access to compute resources like GPUs. Here, the model weights - which can be very large in size - are usually stored using S3-compatible object storage services.
To access data in S3, the development team needs to be aware of configuration details, including the endpoint address, bucket name, and credentials. This can create a security risk as more people will have access to sensitive information. Additionally, this can impact collaboration if changes to the configuration are necessary. Changes must in fact be propagated to all team members to avoid disruptions, such as developers using the wrong bucket. This issue becomes more significant as the number of buckets used by the team increases.
Read our blog to learn how our open source tool, Datashim, can help you address some of these challenges and remove obstacles to collaboration: https://medium.com/ibm-data-ai/simplify-generative-ai-model-development-on-kubernetes-with-datashim-cd2999682807
#GenerativeAI------------------------------
Alessandro Pomponio
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