For tech executives who are looking to expand their in-house machine learning capabilities in a cost-efficient manner. By empowering machine learning scientists with tools to take their work quickly into production we enable data science teams to focus on business problems, not infrastructure problems.
For ML scientists, Shakudo Platform enables scientists to design, develop, deploy and maintain their own work in production. We take the complicated DevOps configurations and infrastructure setups out of the ML lifecycle. Scientists can seamlessly make an impact by iterating their models in production.
For MLOps engineers, Shakudo Platform provides a well-designed pre-configured Kubernetes cluster as a quick start of a working solution. MLOps engineers spend less time figuring out making different solutions works together or building one-off components for ML models. Shakudo Platform exposes robust and DevOps-friendly GraphQL APIs that can be used by consuming teams to interact with AI solutions that are deployed on Shakudo.
Shakudo Platform frees developers from the POC code to production code conversion, and from the headache of figuring out and debugging the scientists' code. Developers can focus on building services that provide more data to the ML models and building Applications that use the ML models' results to make an impact. On Shakudo Platform, developers can use their preferred IDE to utilize the model seamlessly.