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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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Conclusion: Despite decades of investment in new technologies and the promise of 'digital transformation', workforce productivity has languished. The problem is that technological change does not equate to process nor practice change. Put simply, doing the same things with new tools will not deliver new outcomes.
The mass move to working from home has forced a wave of change to practices: people are finally shifting from a sequential approach to work to a genuinely collaborative approach. And this work approach will remain even as staff return to the office.
The emerging wave through 2020 and beyond is process change: continual and iterative digitisation of process. Practice and process changes will be two positive legacies of the pandemic.
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