Manceps is enlisting its data scientists and machine learning experts in the fight against the pandemic.
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.
Explore Our Case Studies
Natural Language Processing to Summarize Medical Records
By using natural language processing and state-of-the-art language models to integrate their wealth of data into a scalable system, a leading health care company was able to automatically structure complex case files into single-page medical narratives.
Our Process
Discovery
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.
Assessment
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.
Design
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.
Implementation
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
Explore Our Case Studies
Natural Language Processing to Summarize Medical Records
By using natural language processing and state-of-the-art language models to integrate their wealth of data into a scalable system, a leading health care company was able to automatically structure complex case files into single-page medical narratives.