Train, Serve, and Manage AI and ML Models On Any Infrastructure
10x faster Go-To-Market
Up to 80% lower TCO
Trusted by teams building the next generation of AI products
“The on demand offering of UbiOps ensures that there’s GPU availability, with the option to scale very rapidly. Also, with UbiOps’ scale-to-zero functionality we don’t need to pay for GPU resources if the application is not being used, e.g., off-season.”
Dr. Alexander Roth
Head of Engineering - Digital Crop Protection at Bayer
”I am a self-taught Data Scientist and have no MLOps knowledge. UbiOps allowed me to reduce a 4-hour process to 15 minutes thanks to its intuitive pipeline construction interface and parallelization method. No worries, the software is easy to pick up.”
Adrien Raynal
CEO Emersio
“Thanks to UbiOps, it will bevery easy and quick for us to scale up. Because of the fact that everything happens in the cloud, it doesn’t matter where the greenhouse is located. We could have customers all over the world.”
Wilmar van Ommeren
Product Innovator Ridder
”Using UbiOps, our team is able to orchestrate all processing steps and data flows seamlessly so we could start using our models while minimizing DevOps costs.”
Hervé Huisman
Founder & CEO at Gradyent
”UbiOps helped us to deploy our product to a production environment in a scalable but cost-effective manner. Their platform works like a charm and is a joy to work with for any data scientist.”
Joris Mollinga
Co-founder at DuckDuckGoose
”UbiOps enables us to develop, deploy and operate any type of data science code, without having to worry about the IT infrastructure. Even when we continue growing in size and amount of data science applications.”
Pieter van der Mijle
Data Scientist at BAM Energy
What our clients say about us
Integrate UbiOps seamlessly into your data science workbench, and avoid the burden of setting up and managing expensive cloud infrastructure.
A central hub for managing your AI
Cost-effective model inference and training
Deploy machine learning models as scalable inference APIs and offload long-running training jobs to powerful cloud hardware, all the while only paying for the time your models are active.
For any AI application
Deploy off –the –shelf models or run custom data science code. UbiOps serves your Python or R code and instantly scales in the cloud, for real-time serverless inference or long-running jobs.
Create single services as well as large modular workflows. Choose between efficient CPU or accelerated GPU instances.
On any infrastructure
Run workloads in our fully managed platform, across hybrid cloud environments or on-premise. All from a single control plane, without compromising security or privacy.
See the value in action
Enjoy benefits backed by real-world results
Why UbiOps?
Built for data science teams
UbiOps automatically turns your code into a service with a secure API and takes care of load balancing, automatic scaling, monitoring and security.
Streamline your MLOps and reduce costs
Save on DevOps investment and manage all your models in one place, complete with version control, logging, auditing and monitoring.
Automate your workflow through our easy-to-use browser interface, your preferred IDE, or a terminal.
Keep track of everything in one place
View metrics on usage and performance. Check if there are any issues with your deployments. Set alerts and notifications.
Get insights into everything that’s going on with extensive logging.
Deploy in no time
Don’t worry about Kubernetes, Docker images, uptime, scaling, or security.
Focus on developing your models and let UbiOps take care of the rest.
Deploy and scale anywhere with superior security
UbiOps will take care of orchestration in the background based on your needs
Deploy on our SaaS solution, hybrid cloud or on-premise
Choose where your AI workloads run. Attach multiple compute environments to the UbiOps control plane. Enhance your local infrastructure with the power of hybrid cloud and optimize for costs and compliance.
Auto-scale with on-demand compute
Choose the compute instances to suit your model and gain access to both efficient CPU and accelerated GPU hardware that scales automatically based on incoming data to and from zero. Only pay when your deployments are running. Don’t pay for inactive models.
Get access to the right hardware on the multi-cloud UbiOps platform
Avoid GPU shortages or local hardware limitations. UbiOps leverages multiple compute environments, offering broad GPU availability and a range of different instances to match your needs.
Improve security, privacy, and compliance
Control how data is processed, where it is processed and whether data is stored on the platform. Your data, your rules.
Create and orchestrate workflows
Build modular applications by re-using and combining multiple deployments in a workflow.
Improve the efficiency and scalability of your ML apps
Each workflow gets its own unique API and each object in a workflow is an isolated service that scales independently.
Construct your own data pipeline
Funnel data through all your deployments or bypass deployments. Embed sub-pipelines or add conditional logic to data flows. Simply drag and drop.
Collaborate simply and effectively
Import or export pipelines directly and share them with your colleagues or other users.
Save time and optimize your computing
Use pre-built operators to help guide and modify data running through your workflows. Customize inputs for each deployment by splitting and reassembling data.
Train your ML models in the cloud
Offload long-running workloads on powerful cloud hardware
Train and deploy your AI faster
Upgrade your training speeds with on-demand access to top of the line GPUs. Save time by standardizing and easily re-using code environments for both training and inference.
Achieve better machine learning performance
Customize your own performance metrics and compare between different training runs as well as across experiments. Find the model that works best for you.
Integrate training and inference
Train, re-train, and deploy into production – all within the same interface.
UbiOps integrates seamlessly with the data scientist’s toolkit
