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MLOps

Transform Machine Learning productivity – our experts will help you move quickly and confidently from proof of concept to operational use.

Go faster with Kainos MLOps

Kainos’ award-winning team of machine learning experts will transform your adoption of data science to drive common policy, process and practice. We help customers realise the operational gains from data science by adopting ML on the cloud. Kainos MLOps drives alignment between data science, engineering and operations teams.

Increase automation

Ensure compliance

Optimise investment by driving internal alignment

Adopt cloud data science at scale

Reduce risk of failure

Embrace standardised ML DevOps pipelines on Microsoft Azure or AWS cloud

Partners

Partner with a low-risk technical expert

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Deliver value quickly
Industry-leading expertise in robust engineering, agile delivery and innovation
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Building customer self-sustainability
Our experts work with your teams to effectively transfer skills using our proven enablement framework
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Proven MLOps framework
Tried and tested framework of playbooks and models for adopting and accelerating enterprise architecture
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Scalable and flexible
1700+ technical experts across DevOps, solution architecture and data science
Case Study
Kainos implemented MLOps for a large general insurer to drive quality and attain economies of scale within its advanced analytics hub
Insurance case study
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We would highly recommend Kainos for their technical expertise and MLOps approach - their model of building our skills through side-by-side working and knowledge transfer has proven to be really effective. We look forward to continuing to work alongside Kainos to scale our MLOps and wider data science capabilities in the cloud.

ICT Programme Manager, Large General Insurer

Speak to a Kainos MLOps expert here

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Insights

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Case study: Intelligent document comparison for HM Land Registry

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Kainos named trusted machine learning partner by AWS

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MLOps: the Kainos approach to bringing DevOps productivity to your Machine Learning teams