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Australian organisations in both public and private sectors enthusiastically identify and implement best practices from around the world. After considerable time and effort has been allocated to implementing these processes and the associated tools the results are all too often less than satisfactory. There are many best practices, frameworks and tools to assist in the optimisation of IT but there are two key problems areas that if overcome, can make a significant difference in the benefits that organisations will derive from best practice implementation.
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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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