Civo ML, powered by Kubeflow,
for managed dev environments
Our fully managed development environment allows you to leverage the scale of Civo compute for machine learning and AI projects.
Build, launch and scale your projects in just a few clicks
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.
Scale up or down depending on the user's needs, making it suitable for small and large projects.
Allows for the easy deployment of machine learning models with a simple and intuitive user interface.
By leveraging the power of the cloud, Civo ML provides a cost-effective solution for machine learning without the need for expensive hardware.
Access your Civo ML project from any location with internet connectivity, eliminating the need for hardware maintenance.
You can also add additional storage from $0.10 per gigabyte per month.
For easy collaboration and sharing of notebooks
Visual Studio Code
For syntax highlighting, debugging, and code refactoring
For data analysis and statistical computing
What is Civo ML?
Civo ML is a fully managed machine learning development environment built on open-source technology powered by Kubeflow. It's designed to help you leverage the scale of Civo compute for your machine learning and AI projects while reducing the necessary skills and costs associated with running high-performance workloads in the cloud.
We offer support for CPU and high-performance GPU compute (coming soon), enabling customers to scale their machine learning and AI projects on Civo's infrastructure and providing seamless interoperability with popular tools and frameworks.
Note that only the CPU is in a publicly available beta at this stage. GPU compute is currently in a closed invite-only beta - join the waiting list here.
How does Civo ML differ from other machine learning services?
Civo ML 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 ML for my projects?
Civo ML 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 ML support?
Civo ML supports many machine learning algorithms, including supervised and unsupervised learning, deep learning, and reinforcement learning, by integrating with various ML frameworks.
Can I use Civo ML to scale my existing machine learning projects?
Yes, Civo ML allows you to scale your existing machine learning projects and take advantage of CPU and the powerful GPU (coming soon) backed infrastructure provided by Civo.
How can I access my notebooks on Civo ML?
Civo ML provides native support for JupyterLab, Visual Studio Code, and RStudio, allowing you to access and edit your notebooks easily.
Does Civo ML come with any pre-trained models?
Civo ML 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.
How secure is my data on Civo ML?
Civo ML takes data security seriously and benefits from the backing of Defense.com. Your data is protected with industry-standard encryption methods, and Civo follows best data storage and access management practices.
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.