Easily integrated with other tools and platforms, such as Jupyter notebooks, RStudio, and Visual Studio Code, providing a seamless workflow.
Access to high-performance computing resources, including CPU & GPU (coming soon) accelerated instances, for efficient data processing and model training.
With a low barrier of entry starting at just $250, our solution adapts to your demands, accommodating both compact and expansive projects seamlessly.
Deploy and launch your machine learning models effortlessly using our intuitive user interface, all in under 10 minutes!
By leveraging the power of the cloud, Civo KFaaS provides a cost-effective solution for machine learning without the need for expensive hardware.
Access your Civo KFaaS project from any location with internet connectivity, eliminating the need for hardware maintenance.
KubeFlow as a Service pricing
3 x Small nodes
|48 GB||12 cores||180GB NVMe||18 TB||
3 x Medium nodes
|96 GB||24 cores||240GB NVMe||24 TB||
3 x Large nodes
|192 GB||48 cores||360GB NVMe||30 TB||
3 x Extra Large nodes
|384 GB||96 cores||540GB NVMe||36 TB||
What is the current product life cycle stage for Civo KFaaS?
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.
How does Civo KFaaS differ from other machine learning services?
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.
How easy is it to use Civo KFaaS for my projects?
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.
What types of machine learning algorithms does Civo KFaaS support?
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.
Can I use Civo KFaaS to scale my existing machine learning projects?
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).
How can I access my notebooks on Civo KFaaS?
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.
Does Civo KFaaS come with any pre-trained models?
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.
GPU vs. CPU in machine learning
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.