Industry analysts including Gartner and Forrester have long noted that many organisations are failing to capitalise on their investment in analytics. Generally speaking, this results from a focus on model development and data science, however this then results in a struggle to integrate the models into business operations — the action that actually unlocks the value from analytics.
长期以来，包括Gartner和Forrester在内的行业分析师都指出，许多组织未能利用其在分析方面的投资。 一般而言，这是由于专注于模型开发和数据科学而导致的，然而，这导致了将模型集成到业务运营中的努力-这种行为实际上从分析中释放了价值。
ModelOps is a framework or practice that has emerged to address this challenge and is inspired by the success of DevOps. Its focus is operationalising analytics, i.e. taking models from development to production, and therefore transforming modelling from an academic exercise to an economic benefit. Effectively, it activates the value of analytics by applying data science to decision-making within the organisation.
ModelOps是为应对这一挑战而出现的框架或实践，并受到DevOps成功的启发。 它的重点是对分析进行操作，即从开发到生产都采用模型，因此将模型从学术活动转变为经济利益。 通过将数据科学应用于组织内的决策，它有效地激活了分析的价值。
I have often heard ModelOps described as ‘sophisticated model management’. However, it is much broader than model management because it is supported by a wide range of technology, from data to decisions. It is also known as MLOps, DeepOps or AIOps and simply put is a framework that helps organisations take models from development to production effectively.
我经常听到ModelOps被描述为“复杂的模型管理”。 但是，它比模型管理要广泛得多，因为它得到了从数据到决策的广泛技术的支持。 它也被称为MLOps，DeepOps或AIOps，简单地说就是一个框架，可帮助组织有效地将模型从开发转移到生产。
To better understand the complexities involved and what ModelOps looks like in practice, I’m going to cover the aspects that should be addressed in a series of articles. These articles cover the benefits, organizational framework, the supporting technologies and the sophistication levels of ModelOps. The content described draws on experiences helping organisations of many sizes, across many industries, assessing and implementing their ModelOps frameworks. As well as conversations with peers and research from within the industry.
为了更好地理解所涉及的复杂性以及ModelOps在实践中的外观，我将在一系列文章中介绍这些方面。 这些文章涵盖了ModelOps的好处，组织框架，支持技术和复杂程度。 所描述的内容借鉴了经验，可以帮助许多行业的各种规模的组织评估和实施其ModelOps框架。 以及与同行的对话和行业内的研究。
There are four key benefits of ModelOps:
ModelOps有四个主要优点：
1. Faster time to value, by streamlining the analytics lifecycle and reducing the time between development and deployment;
1.通过简化分析生命周期并缩短开发与部署之间的时间来缩短实现价值的时间；
2. Better and more justifiable business outcomes, by bringing more governance to the analytics lifecycle and continuously monitoring the performance and business impact of the models deployed;
2.通过在分析生命周期中引入更多治理并持续监控所部署模型的性能和业务影响，来获得更好和更合理的业务成果 ；
3. The ability to scale analytics through automation and repeatability means doing more with the same resources, through more effective collaboration between the business, data scientists, operations and IT.
3. 通过自动化和可重复性扩展分析的能力意味着通过业务，数据科学家，运营和IT之间更有效的协作，利用相同的资源完成更多工作。
4. Embedding analytical insight into every business decision and customer touchpoint, driving better, real-time and automated decision-making.
4.将分析洞察力嵌入到每个业务决策和客户接触点中，从而推动更好，实时和自动化的决策制定。
Of course, it is possible to develop and deploy analytical models without ModelOps. However, its use makes the entire process much more efficient, improving the speed, volume and governance of models being deployed in production. Additionally, the question of where ModelOps fits with DevOps and DataOps often arises when speaking with organisations. Combining the three allows organisations to take full advantage of their analytics investment and genuinely operationalise analytics, even though both DataOps and DevOps have effects well beyond this.
当然，无需ModelOps也可以开发和部署分析模型。 但是，它的使用使整个过程更加高效，从而提高了生产中部署的模型的速度，数量和治理。 此外，与组织进行交谈时，经常会出现ModelOps与DevOps和DataOps相适应的问题。 结合使用这三种方法，即使DataOps和DevOps的作用远不止于此，组织也可以充分利用其分析投资并真正实现分析的操作。
It is worth pointing out that ModelOps is not relevant for all organisations. Organisations will only get the full benefits of this approach if they are using predictive analytics. Those doing historical reporting, data exploration and off-line statistics should therefore take time to identify if new use cases for predictive analytics are valuable. If so, they can use ModelOps when developing these use cases to maximise their efficiency and ability to become more sophisticated in the future.
值得指出的是，ModelOps并非与所有组织都相关。 如果组织使用预测性分析，则它们只会获得这种方法的全部好处。 因此，进行历史报告，数据探索和离线统计的人员应该花一些时间来确定预测分析的新用例是否有价值。 如果是这样，他们可以在开发这些用例时使用ModelOps，以最大程度地提高效率和将来变得更复杂的能力。
It is also important to note that ModelOps is not simply DevOps for models. Models are different from traditional software components. Model development needs specialised analytics skills and is an experimental process. Model deployment is also extremely complex. Models need to evolve over time to reflect changes in market conditions and the underlying assumptions about the data. They therefore need to be continuously monitored and retrained to avoid any degradation in their performance and it is necessary to establish processes to continuously monitor and retrain them.
同样重要的是要注意，ModelOps不仅仅是针对模型的DevOps。 模型不同于传统的软件组件。 模型开发需要专业的分析技能，并且是一个实验过程。 模型部署也非常复杂。 模型需要随着时间而发展，以反映市场状况的变化以及有关数据的基本假设。 因此，需要对其进行连续监控和再培训，以避免其性能降低，并且有必要建立持续监控和再培训它们的过程。
Once organisations have decided that they can benefit from ModelOps, they need to think about how to use the approach. ModelOps is NOT a one-size-fits-all approach. The level of sophistication needed is guided by the organisation’s business objectives and its existing analytics capabilities and cultural readiness. However, even for organisations that need to reach the most advanced approach, a stepped progression through the stages will improve adoption. Crucially, using the right level of ModelOps sophistication will help the organisation to gain value from its analytics and reduce the risk of failure. We will go into more detail of what these sophistication levels look like later in a future article.
一旦组织决定可以从ModelOps中受益，他们就需要考虑如何使用该方法。 ModelOps并不是一种万能的方法。 所需的复杂程度由组织的业务目标及其现有的分析能力和文化准备程度决定。 但是，即使对于需要采用最先进方法的组织，分阶段逐步进行也可以提高采用率。 至关重要的是，使用正确级别的ModelOps可以帮助组织从其分析中获取价值并降低失败的风险。 在以后的文章中，我们将更详细地介绍这些复杂程度。
What does this mean in practice? It means that a holistic approach, combining people, process and technology with a supportive culture, is needed to successfully implement ModelOps. Organisations should define their process based on their business strategy, enabling this with a supportive culture. This will allow the organisation to define a consistent technology stack to facilitate the process and reduce the chances of silos and duplication of work.
在实践中这意味着什么？ 这意味着需要成功地将人员，流程和技术与支持性文化相结合的整体方法。 组织应根据其业务策略定义流程，并通过一种支持性的文化来实现这一目标。 这将使组织能够定义一致的技术堆栈，以简化流程并减少孤岛和重复工作的机会。
The next article in this series will discuss this approach in more detail.
本系列的下一篇文章将更详细地讨论这种方法。
翻译自: https://medium.com/@reece.clifford/introducing-modelops-to-the-organisation-what-it-is-and-its-benefits-41375d5edae5
