In previous sections, we have discussed a range of non-technical issues that can present barriers to introducing machine learning methods for editing. Some of these issues may also arise when trying to use ML methods for other parts of the statistical production process. For all these issues, it is clear that careful planning of any machine learning project is necessary for success. This might include factoring in costs to acquire and label training data, planning to bring in different teams with different expertise at the right points in the project, scheduling project milestones to facilitate the involvement of busy subject matter experts, or including time and resources to educate future users. Building good relationships and having clear lines of responsibility between methodology, data science, business and IT teams is vital, as is making sure that appropriate IT environments and resources are available to support the demands of an ML project. Different approaches may be needed to carry out a proof of concept compared to introducing methods into production, but the latter should not be ignored when planning the former in order to facilitate a smoother introduction into production later.

The implementation of ML Operations (MLOps) in statistical offices represents a pivotal transition from traditional ways of implementing methods to more advanced and automated processes. MLOps also provides a structured framework for embedding Responsible AI principles. These principles ensure ethical, transparent, and accountable use of AI and machine learning, covering aspects such as fairness to prevent biases in models, accountability in development and deployment, transparency in decision-making processes, adherence to ethical standards in data usage and protection of sensitive information. MLOps in statistical offices is not just a technological upgrade but a comprehensive strategy towards more responsible, efficient, and advanced data processing and analysis. Along with responsible AI and other MLOps principles, it covers IT infrastructure, tools, processes, and roles. This transition is essential for statistical offices to remain relevant and effective in a data-driven era, guaranteeing the provision of accurate, reliable, and insightful statistics. Because of this we include some reflections on what is needed to set up MLOps in the Appendix 1.