The biggest challenge for ML in enterprise is getting systems/models well... deployed. It doesn't have to be that way, we will help you with every step of your delivery pipeline. Empower your developers to release often!
Maybe you want to manage non-native resources using the Kubernetes API Server, or you need to integrate with legacy applications. We got you covered, we can extend Kubernetes to make it speak your language!
You don't want data scientists and developers wasting their time on repetitive tasks. We can automate any piece of their workflow, from local development environments setup to cloud resources provisioning.
We help you standardize the way you train, deploy, and monitor your models. With a Pipeline as Code + GitOps approach your engineers can rely on optimized repeatable patterns of CI/CD.
We can design, implement, and help you maintain your cloud resources. We specialize in the AWS stack and we are always on top of best practices. We will deliver you reusable configurations (Infrastructure as Code) with finely crafted documentation.
It's hard to do Kubernetes, especially for Machine Learning. We design your clusters and build the automation necessary to get them ML ready: notebooks servers, Kubeflow, GPU's, RBAC, Helm, Operators, etc.
If you have very specific automation/ML needs that cannot be fulfilled by available commercial or open source software, we can code it for you! We have plenty of experience implementing cloud native solutions.