Kubeflow as a Service

The CPU is in a publicly available beta at this stage, to have access, create a Civo account, or access your Civo dashboard. GPU compute is currently in a closed invite-only beta.

Civo KFaaS sets itself apart with one-click access to CPU and GPU (coming soon) backed machine learning and AI tooling, native support for Jupyter and Visual Studio Code, and acceleration by Civo infrastructure for optimal performance. In addition, its Kubeflow foundation, easy integration with tools like Jupyter, RStudio, Visual Studio Code, TensorFlow, and PyTorch, and focus on providing a cost-effective and user-friendly solution make it an ideal choice for both data scientists and engineers.

Civo KFaaS is designed to be easy to use, with a user-friendly interface, one-click access to powerful CPU and GPU (coming soon) backed machine learning and AI tooling, and seamless integration with popular tools and frameworks.

Civo KFaaS supports a wide array of machine learning algorithms, encompassing supervised and unsupervised learning, deep learning, reinforcement learning, and Large Language Models (LLMs). This versatility is achieved through our integration with various ML frameworks.

Yes, Civo KFaaS allows you to scale your existing machine learning projects and take advantage of CPU and the powerful GPU backed infrastructure provided by Civo (GPU coming soon).

Civo KFaaS offers native support for JupyterLab, Visual Studio Code, and RStudio, ensuring effortless access and editing of your notebooks. Additionally, you have the flexibility to bring your own containers, tailoring the environment to your specific needs.

Civo KFaaS does not come with pre-trained models, but it provides access to a wide range of machine learning and AI tooling, allowing users to train their own models using their preferred ML frameworks.

When it comes to machine learning, the choice between GPUs and CPUs often comes down to the specific needs of a task. GPUs, known for their parallel processing capabilities and a large number of cores, are highly effective for complex computations and handling vast amounts of data, making them ideal for training deep learning models. On the other hand, CPUs, which have fewer but more powerful cores, offer better performance for simpler, sequential tasks and tend to be more cost-efficient for inference. In the end, the decision to use GPUs or CPUs for machine learning will depend on the task at hand, the scale of the workload, and the desired balance between performance and cost.