As the use of AI and machine learning continues to mature throughout the business world, scaling these capabilities becomes critical. After all, the value these emerging technologies provide businesses wanes if not delivered in a real-time fashion. A financial trader needs the latest AI-driven insights on securities markets as quickly as possible.
To meet this growing corporate need, a process-focused encapsulation of machine learning, known as MLOps, is entering the market. What follows is an overview of MLOps to see if it makes sense as part of your company’s IT operations. Since the operational model works for software development – DevOps being an obvious example – it makes sense for machine learning to follow a similar approach to efficiency.
An Overview of MLOps
MLOps stands for machine learning operations, which follows a similar etymology as ITOps and the previously mentioned DevOps. The prime directive for most businesses adopting this approach is scaling and improving the efficiency of their use of AI. MLOps contains four main areas, which are similar to many other software development process models.
These four major areas of MLOps are:
- Production Model Deployment
- Production Monitoring
- Model Lifecycle Management
- Model Governance
It’s important for companies adopting MLOps to devote the necessary resources to all four areas. A diligent and thorough approach is a must. Let’s take a quick look at each.
Deploying machine learning models needs to be an easy process. Obviously, leveraging automation in this regard improves efficiency. In a similar manner as DevOps, getting the teams responsible for model development, data analytics, and IT operations to work closely together is a must.
Using a tool designed specifically for monitoring ML models in production is critical. Data drift, model accuracy, and overall service health are some of the key metrics a ML modeling tool needs to provide. Fast alerting of relevant ITOps and other teams is also essential.
Machine Language Model Lifecycle Management
A MLOps process must support models after they are installed in production. The ML model lifecycle management process primarily includes development and QA, with the latter role performing A-B testing between new and older versions of models. The ability to quickly rollback models is critical.
ML Model Governance
Machine learning model governance includes everything from access controls to production logs and reporting. Clearly-defined responsibilities for the roles and responsibilities of the MLOps team also helps ensure everything runs smoothly with an eye towards continuous improvement.
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