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Implementing machine learning operations (MLOps) is complicated by several challenges: the number of the stakeholders involved in a project; the shortage of people with the necessary skills; the scope of regulatory compliance; validation of the machine learning (ML) model; and model degradation issues. Considering how these challenges will be addressed is a vital precursor for the successful implementation of MLOps.
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Organisations must evolve practical and sustainable governance when incorporating low-code platforms into their enterprise architecture (EA). The majority of organisations will use more than one low code platform on their digital transformation journey. As a result, governance will need to encompass tenets that determine which tools (and thus skills and teams) are most appropriate for which types of applications and workflows.
Traditional development practices have been supplanted by the DevOps movement over the past decade. The next evolution is the movement towards DevSecOps where security is integrated across the development lifecycle.
DevSecOps is not just a matter of buying the latest tooling and running the developers through some training. It requires commitment, not just from the technology group as a whole but from the business leaders themselves.
It is as transformative a project for an organisation as is a move from on-premise to Cloud. Poorly managed or even unplanned DevSecOps can have a negative impact on the development capabilities within an organisation.
Conclusion: Machine learning operations (MLOps) adapts principles, practices and measures from developer operations (DevOps), but significantly transforms some aspects to address the different skill sets and quality control challenges and deployment nuances of machine learning (ML) and data engineering.
Implementing MLOps has several benefits, from easing collaboration among project team members to reducing bias in the resulting artificial intelligence (AI) models.
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