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Its taking us quite long to get our models into production and derive impact from them. Is there a way we can empower data scientists to take charge of this process?
ML Engineers are heavily reliant on DevOps/platform teams for infra needs to train or deploy models
We want to use our standard Kubernetes infrastructure for ML training and deployments
Data scientists don’t want to deal with infra or YAML
We want our data to stay inside our own cloud or on-prem
Models are deployed with autoscaling configured using HPA - but autoscaling is very slow because of download time of models.
We want to host Jupyter notebooks and make it self serve with flexibility to provision resources, while putting some cost constraints on cost and security.
How to keep track of all models inside the company in one place, figure out which ones are deployed in what environment?
How do I mirror or split traffic to my new version of the model so that we can test it on online traffic before rolling it out completely?
We want to use hardware and compute across clouds (AWS, GCP, Azure) and on-prem. How do I connect them so that developers don’t need to worry about the underlying compute and seamlessly move workloads from one environment to other?
We want to use the power of LLMs for our business but we cannot let the data out of our environment. Is there any way to utilise the power of LLMs without sending my data to OpenAI?
How do I allow all my developers to quickly try out different LLMs and see what results they can get out of it?
We are incurring a lot of cost on our ML infra and its becoming difficult to track and reduce it.