How MLOps helps businesses realize machine learning’s full potential

Machine learning has the potential to reshape the world in many areas, changing the way we use and understand data.

For instance, healthcare companies are tapping into machine learning technologies to deliver faster, more accurate diagnoses for everything from cancer to Covid-19, while security companies are using various algorithms to train surveillance systems to recognize threats.

In 2019, data science topped the list of LinkedIn’s emerging job rankings, with hiring growth in the industry at 74%.

However, even for businesses with expert teams, less than half of data science projects end up being fully deployed.

For all the benefits that machine learning can afford companies, actually taking it to the last mile and using it effectively is surprisingly difficult.

The challenges of machine learning

Production machine learning, or production ML, is the process of putting machine learning into practice and benefiting from it. However, a whole array of problems arise when it comes to implementation.

Issues often arise in the areas of data management, data life cycles, and the deployment and management of machine learning models.

Data scientists, for instance, have difficulty implementing these models into production environments, as that is not the main focus of their work, while operations staff are often unsure of how to best integrate machine learning models into their existing systems.

To maximize the advantages of machine learning, it’s necessary for businesses to unify the different people involved in production ML, such as data scientists, IT engineers, developers, and the operations team, and establish a streamlined process across their respective domains.

This is where MLOps comes in.

What is MLOps?

Machine learning operations, or MLOps, provides a way for companies to deploy and manage machine learning models more effectively.

This was the reason why enterprise AI platform DataRobot acquired ParallelM – a MLOps software company – in 2019.

ParallelM co-founder and CEO Sivan Metzger, who is now the managing director of MLOps at DataRobot, was inspired by his own experience of working with data science teams at his previous job in the advertising technology industry.

Sivan Metzger, managing director of MLOps at DataRobot / Photo credit: DataRobot

“In earlier companies I worked at, we had a team of data scientists, and they were doing wonderful work, but none of their work was seeing the light of day because collaboration between them and the operations team was unattainable,” Metzger shares.

Think of MLOps as DevOps for machine learning models. It combines the disparate processes involved in deploying machine learning systems by merging development and operations into one seamless unit.

“MLOps connects the relevant data together with the models and runs and manages them in the live environment where predictions need to be made, feeding into the relevant business processes,” explains Metzger.

The importance of MLOps

Machine learning models are different from regular software applications, which means typical parameters used to test the effectiveness of a program are no longer applicable.

For example, machine learning systems are very sensitive to data and have difficulty in navigating production situations where context is shifting, says Metzger.

Unlike software, where it’s easy to check if it’s doing what it’s supposed to do, there are no real guidelines to analyze if machine learning models are generating the right predictions, thanks to the probabilistic nature of machine learning.

With MLOps, developers can monitor and upgrade the models while they are running in production, without interrupting services to business operations, much like how software updates are barely a blip on our radars.

But as machine learning becomes more prevalent, regulatory guidelines surrounding its use are getting stricter, which is a problem for operations teams as well.

To solve this, MLOps also includes the notion of “production model governance,” allowing companies to limit, manage, and track who can make changes to a machine learning model.

These changes are then logged, and the operations team can examine them to see if they align with regulations, querying the reasons behind the adjustments if necessary.

Data scientists, on the other hand, can focus on getting models working accurately, without having to worry too much about meeting requirements or fixing deployment issues.

How businesses benefit

According to Metzger, all companies stand to gain from using MLOps. Data is the new oil, and many businesses need to tap on the affordances of machine learning to progress and remain competitive. It is vital to use the right strategies and technology in implementing machine learning.

Photo credit: Monsit Jangariyawong / 123RF

MLOps allows these entities to divide the roles in all aspects of production ML more clearly, says Metzger. It also “tightens and closes the loop” when it comes to the collaboration between the different teams inherently involved..

For instance, instead of having complicated back-and-forths between the data scientists and operations staff, where people have their own approach towards the work and use different software and programs, everything can be done through a centralized MLOps platform.

One company that has benefited from such practices is Wisely, an AI-powered fundraising service. ParallelM worked with Wisely prior to its acquisition by DataRobot, and helped integrate MLOps into its development process.

In doing so, Wisely was able to deploy and optimize the algorithms used to scale its fundraising productivity tool, without requiring any additional resources.

The future of MLOps

As the data science industry focuses on scalability, greater collaboration, and the need for machine learning models to be production-ready, MLOps is becoming a vital tool and process.

And as machine learning becomes more prevalent across various industries, companies need to begin laying down the foundations of the technology, getting non-data scientists involved in the process right from the get-go.

If businesses want to fully capitalize on the potential of machine learning, they must also begin from the ground-up.

“You don’t build a house in a day. You first build the foundation, and a strong MLOps strategy is the foundation required for machine learning initiatives to ultimately become successful,” says Metzger.

DataRobot is an enterprise AI platform that aims to democratize data science with end-to-end automation for building, deploying, and managing machine learning models.

Its new MLOps offering provides a centralized hub for deployment, monitoring, management and governance of machine learning models created from a variety of tools.

Find out more about MLOps through DataRobot’s ebook on how MLOps can help companies tackle real-world machine learning issues that may be holding back AI projects.