Manceps and Canonical Work Together to deliver stable, scalable AI solutions using Kubernetes.

The complexity of AI/ML spans infrastructure, operations, machine learning model development, model evolution, model deployment and updates, compliance and security. To add to the challenge, the speed of innovation in open source machine learning means that complexity is compounding annually.


That’s why it pays off to bring our data scientists and Canonical's infrastructure tools in early. The right resources and experts accelerate delivery and keep your team focused on your particular data streams and particular business objectives. Together, we help deliver an accelerated design sprint with your analytics and infrastructure teams.


At the end of the engagement you will have a pattern for productive AI development — spanning developer workstations, machine learning infrastructure (in the cloud or on-premises), and AI applications — delivering daily insights, powered by your data.


With our structured program, we can approach any situation and provide the infrastructure and capabilities that your business needs. Proven best practices mean that we can take the guesswork and experimentation out of your AI adoption, and fast-track you to value without breaking your budget.

Manceps + Canonical

As a trusted Canonical Partner with data analytics and machine learning specializations, Manceps has the knowledge and experience to help you deploy artificial intelligence using Kubeflow on Ubuntu.

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Our Process


Following a series of AI strategy sessions to set the stage, partner data scientists and data engineers will explore your business requirements, expected outcomes, data sources, potential opportunities and risks. We will discuss machine learning use cases for your industry and explore the specific business applications that can add the most value.


Google-certified experts will provide initial feedback based on the Discovery phase. They will report on the data and make solution recommendations. They will discuss building a strategy roadmap for the project based on your specific business case. Any changes to your infrastructure should be highlighted during this step.


Prototype new solutions and identify a candidate solution that seems to be the best fit for the use case under consideration. Data engineers will prepare the data, data scientists will discuss and select appropriate features and machine learning algorithms, and machine Learning engineers will design, build and perform preliminary tests on your prototype neural network. Time permitting, the iterative process of design and discovery on the data and the neural network model will start.


Complete the design process and begin training and testing your AI model until it reaches the desired accuracy threshold. A kubeflow pipeline that will put your model into a suitable environment for testing and feedback from additional stakeholders will be built. Domain experts will offer guidance on assessing machine learning predictions and putting discovered insights into action.

“Canonical shares our core values and commitment to the open-source community, and we are excited to have them as a strategic infrastructure partner. Not only do they offer a popular enterprise OS and a proven private cloud infrastructure, but they also make deploying and managing ML stacks on Kubernetes simple, scalable and portable with Kubeflow.”

— Al Kari, CEO of Manceps

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