A 2019 Gartner survey showed that CIOs finally identify AI as a top game-changing technology – one that many plan to deploy in the next 12 months. While enterprises have rapidly increased their adoption of AI, initiatives have been scattered across the organization, built on different platforms, using different processes, and advancing at different paces. Now both analysts and enterprises are becoming aware of the fact that there’s a way to organize enterprise AI into a strategic journey through ModelOps.
In the same way that DevOps and agile software development helped organize the modernization of enterprise software, ModelOps is a foundational capability that aligns business imperatives, organizational structures, and technical sophistication to bring order to the “wild west” of enterprise AI.
There are three main reasons why enterprises today need ModelOps:
- Strategic imperative: Enterprise AI is transformative, with the potential to fundamentally change how the organization runs their core business processes and decision-making in order to sustain their competitive advantage. Among enterprises today, recognition of this fact is strikingly greater compared to 2-3 years ago, indicating that enterprises are ready to take the steps necessary to make their AI investments work harder for them. However, this also introduces greater accountability to ensure that AI investments are seeing the return expected at the C-level. ModelOps is the organizing driver that ensures AI models are actually put into business and continuously maintained to drive optimal sustained results.
- Organizational chaos: When enterprises first started building their AI capabilities, there were likely many different lines of business creating their own data science projects using different model factories. Likewise, various IT infrastructure groups have prepared for the deployment of AI in different ways. ModelOps aligns disparate enterprise teams by operationalizing all models in a consistent way.
- Technological advancements: In the past few years, there’s been a lot of fast-moving, high-velocity innovation across data science, DataOps, ITOps, and DevOps. ModelOps creates structure and organization around these technologies, while simultaneously allowing data science teams to continue to leverage the latest innovations in the community, and DataOps and other IT teams to leverage their existing investments in data systems, infrastructure, and shared services.
Similar to how DevOps capabilities organized the chaos in the software world, ModelOps brings order – and thus scalability and control – to enterprise AI via several critical capabilities:
- Providing a consistent representation of a model, regardless of the model factory in which it was created, the platforms it uses, or the business use case it serves.
- Enabling consistent processes to productionize, monitor, and continuously improve a model. Across all models, all business units, all technology. This includes standard processes to conduct reproducible testing of models in target environments using golden data sets…not on a local laptop.
- Providing a central model catalog to understand all the models running across the organization, including their assets, history, and performance.
- Surfacing key metrics around interpretability and ethical fairness that expose an organization to regulatory or brand risk, if not managed properly.
- Enabling 24×7 visibility and support to proactively ensure that all models are performing optimally for the core business processes they serve.
As we enter 2020, most organizations have moved past the pioneering phase of AI and have had enough of the chaos and uncertainty that has reigned in the wild west of AI. They’re ready to organize their greatest technological investments to drive accountability and scale. ModelOps is the capability that makes that possible. To learn more about how ModelOp can help your enterprise unlock the promised value of its AI and Machine Learning investments, contact us.
