What is Machine Learning Operations (MLOps)?

Machine Learning Operations (MLOps) is a paradigm that aims to deploy and maintain machine learning models in production reliably and efficiently. Machine learning modeling and experimentation is vitally important. Data scientists need the space and time to build complex mathematical formals to predict outcomes based on a given dataset.

Ultimately, though, the goal is to put those models into production. A model that stays in a laboratory or exploratory environment might show some benefit. But, until the model is in a real-world environment, that benefit will be minimal.

“Machine learning models are great, but unless you know how to put them into production, it’s hard to get them to create the maximum amount of possible value.” – Andrew Ng, Stanford University, “Machine Learning in Production”

Unfortunately, Gartner recently reported that only 53% of AI/ML projects make it into production.

MLOps as a discipline provides methods of productionizing ML models so that more make it into production.

What is Enterprise-Ready MLOps (eMLOps)?

However, MLOps alone may not be enough for the enterprise.

For example, some propose that MLOps means apply DevOps principles of CI/CD to ML models. Others suggest that MLOps means providing a feedback loop so that ML models can be retrained faster. Still others see MLOps as a way to monitor the output of ML models to determine if the model or features have drifted.

All of these are valid methods of improving the productionization of ML models. Alone, they are not enough for the enterprise.

“It’s one thing to create a model that works inside a Jupyter notebook; it’s a very different scenario to put it into a production system and then the production system uses the model to make decisions, solve business problems, and create revenue.” -Noah Gift, Executive in Residence at Duke University 

Enterprise-ready MLOps (eMLops) solutions apply enterprise processes and operations to machine learning models in a manner that maximizes the benefit to the enterprise. eMLOps involves multiple levels, products, services, and components that work together to facilitate the deployment, management, and scaling of machine learning models in production enterprise environments.

eMLOps Solutions to Match Your Enterprise Requirements

Your enterprise is composed of a unique set of people and processes and tools, with unique requirements. There is no software or platform tool that completely meets the needs of your enterprise in any area. The same is true for machine learning.

CtiPath’s eMLOps solution takes a layered approach to productionizing your ML models and workloads. (See “Enterprise-Ready MLOps has Layers“.) We work with you to determine which layers are important to you and your business, and design a solution and maturation process to meet your needs.

Figure: Simplified eMLOps Layered Solution

We accomplish this by providing a myriad of services that match the multiple-layered approach of productioninzing ML models.

eMLOps-related Services

eMLOps is not a service or a production. But eMLOps integrates a multitude of productions, processes, and services to meet the needs of your enterprise’s unique environment and objectives.

Data Services

Data is the lifeblood of any machine learning projects. Data services encompass the tools, processes, and infrastructure required to collect, store, preprocess, and manage data. In the context of eMLOps, data services are crucial for ensuring that models are trained on accurate, relevant, and high-quality datasets, and that the data is prepared and delivered to the productionized ML model.

Integration Services

Machine learning models do not operate in isolation. They must be integrated into existing enterprise IT ecosystems, which often include databases, applications, infrastructure, and other services, as well as processes and governance. Integration services facilitate this by ensuring that models can seamlessly communicate with these components. For eMLOps projects, integration services can take on a more complex role as different teams in the enterprise rely on different tools and processes.

DevOps Services

DevOps services in eMLOps focus on automating the deployment, scaling, and monitoring of ML models and systems. They ensure that models can be rapidly deployed into production, updated as new data becomes available, and scaled to meet changing demands. Beyond the model, infrastructure, data pipelines, analytics, and other layers of eMLOps solutions benefit from CI/CD processes as well as dev and qa environments included in DevOps.

Managed Services

Logging and monitoring are critical components of eMLOps systems because they provide the visibility, traceability, and control needed to ensure the reliable and efficient operation of machine learning models in production. Continuous monitoring of machine learning models in production helps to ensure that they perform as expected. This includes tracking key metrics like prediction accuracy, response time, and error rates. Managed services is also important in tracking the performance of the data pipeline and underlying the project infrastructure.

LifeCycle Services

Lifecycle services in eMLOps encompass the end-to-end processes involved in designing and deploying the layered solution of enterprise-ready machine learning projects. It would also include designing a maturation cycle, beginning the minimum viable solution and working toward a solution that meets all the enterprise requirements.