#Is critical ops cross platform software#

In order to solve common pain points when it comes to model operationalization - such as the long delay between initiating a data science project and deploying the model - companies are taking a four-step approach: build, manage, deploy/integrate, and monitor. For such applications, the ML Ops lifecycle must be designed to enable transparency and explainability when it comes to various risks. ML models that are people-facing must be unbiased, fair, and explainable that is what the public demands and regulatory agencies and bodies increasingly require. In fact, many organizations have a dedicated Model Ops Engineer to facilitate this process. Model Ops requires orchestration and coordination of many different personas within an organization including data engineers, data scientists, business users, IT operations, and app developers. While data science and ML processes are focused on building models, Model Ops focuses on operationalizing the entire data science pipeline within a business system.

Organizations need to realize the value of data science and machine learning models holistically, rather than as simply a process of developing models. What’s more, Model Ops encompasses various management aspects, such as model versioning, auditing, monitoring, and refreshing to ensure they are still delivering positive business value as conditions change. Model Ops is a cross-functional, collaborative, continuous process that focuses on managing machine learning models to make them reusable and highly available via a repeatable deployment process. They also provide critical insights for managing the potential risks of model-based decision making, even as underlying business and technical conditions change. Model Ops (aka ML Ops) ensures that models continue to deliver value to the organization.

#Is critical ops cross platform software#

The four main steps in the Model Ops process - build, manage, deploy/integrate, and monitor - form a repeatable cycle that you can leverage to reuse your models as software artifacts.

Model Ops is the process of operationalizing data science by getting data science models into production and then managing them. Model Operations, or Model Ops, is the answer. You wouldn’t spend all this time and money on creating ML models without putting them into production, would you? You need your models infused into the business so they can help make crucial decisions. Just as you wouldn’t train athletes and not have them compete, the same can be said about data science & machine learning (ML).