Introducing ASI Machine Learning Workspaces
The gap between a good idea and a working AI model is often… infrastructure.
You’ve got notebooks, datasets, and a model plan. But then you hit the real-world hurdles: Who’s setting up the GPUs? Why is CUDA broken again? Can we share this environment with the rest of the team?
We hear this from customers all the time. So we’ve built something to remove that friction.
What is ASI Machine Learning Workspaces?
ASI Machine Learning Workspaces is a fully managed, GPU-accelerated environment for AI and machine learning workloads, delivered and supported by ASI Solutions.
At its heart, you get Jupyter notebooks running on GPU-backed Kubernetes infrastructure, pre-configured ML frameworks like PyTorch and TensorFlow, and Kubeflow for pipelines, experiments, and model serving. ASI manages everything from CUDA drivers to GPU scheduling, so your team gets a ready-to-go environment for research, prototyping, and training — without needing to be Kubernetes, GPU, or DevOps experts.
Who is this for?
These workspaces are designed for organisations wanting to accelerate AI/ML without building and maintaining a complex platform themselves. That includes universities and research institutes running deep learning, NLP, and computer vision projects, enterprises experimenting with generative AI and forecasting, data science teams tired of “it works on my machine” problems, and startups who want to move fast while staying secure.
If you’re currently juggling ad-hoc GPU boxes, fragile conda environments, or long-running jobs on shared hardware, this is for you.
What you get out of the box
GPU-accelerated Jupyter notebooks — Spin up a notebook, select a GPU-backed environment, and start coding. No driver installs, no CUDA wrangling. Support for Python, R, and Julia comes standard, with PyTorch and TensorFlow pre-installed.
Fully managed infrastructure — We take care of the plumbing so your team can focus on models, not machines. ASI manages Kubernetes orchestration, GPU provisioning, NVIDIA device plugins, and Kubeflow integration. No more hunting for the one machine with the right drivers or losing days to environment rebuilds.
Kubeflow for ML pipelines — When you’re ready to move from experiments to something more repeatable, Kubeflow is built in. Use it to orchestrate end-to-end ML pipelines, track experiments and model versions, and deploy model serving workloads. This gives you a natural path from notebook to pipeline to production-ready workflow.
CUDA-ready, multi-GPU support — Each workspace is backed by GPU nodes with the CUDA toolkit pre-installed and configured, multi-GPU support for distributed training, and resource management handled by the platform.
Secure multi-user environments — Machine learning in the real world is a team sport. You get isolated environments per user or team, authentication aligned with your organisation’s policies, and resource quotas to prevent noisy neighbours.
Why this matters
Here’s what changes in practice. Onboarding time drops from weeks to hours — new staff, students, or collaborators log in and start working straightaway. Infrastructure overhead disappears, so IT teams don’t need to be GPU, Kubernetes, and ML stack experts all at once. Experiments become repeatable and shareable, reducing those frustrating “it only works on my laptop” moments. And you get a future-proof foundation for AI as tools and frameworks evolve.
Real-world use cases
A few scenarios where ASI Machine Learning Workspaces shine: research groups running long simulations or deep learning training on shared GPU infrastructure, data teams building forecasting models for demand or pricing, computer vision projects working with high-resolution imagery, and NLP experiments using large transformer models that require serious GPU horsepower.
Ready to accelerate your AI research?
We built ASI Machine Learning Workspaces to remove the biggest barriers between ideas and working AI systems. If you’d like to give your team on-demand access to GPU-accelerated notebooks, stop worrying about CUDA and cluster setup, and move towards repeatable, production-ready ML workflows, we’d love to show you what this looks like in your environment.