Powering Manufacturing With MLOps

Machine learning is one of the most disruptive technologies across industries today. Despite this versatility and potential, many organizations struggle to capitalize on this technology’s full potential, especially in sectors like manufacturing that lack widespread ML skills and knowledge.

High upfront costs, complex deployments, data quality issues, and meager returns on investment (ROI) hinder manufacturing ML projects. If the industry hopes to implement this technology effectively, it needs a better approach to developing and using these models. MLOps offers an ideal solution.

What Is MLOps?

As the name suggests, MLOps borrows heavily from the practice of DevOps, which now, according to Statista, accounts for 47% of software development projects. Just as DevOps marries development and operations to promote continuous integration (CI) and delivery (CD) in software development, MLOps applies CI and CD to ML model programming and deployment.

Because ML models are unique from other types of software, there are some important differences between MLOps and DevOps. Most notably, CI and CD must account for the relationships between the code, model, and input data, not just the code itself.

MLOps continually evaluates and tests machine learning models and data on top of the development pipeline that produces these models. Unlike many conventional approaches to ML deployment, it also automates much of the model training and testing phases. This automation and close collaboration between data scientists, software engineers, and end users bring the benefits of DevOps to machine learning. 

How MLOps Can Benefit Manufacturing

With many manufacturers using ML for supply chain management, factory optimization, and similar tasks, MLOps offers many advantages to the industry. Here are some of the most significant.

Faster ML Deployment

Like DevOps, MLOps can shorten development and deployment timelines. That advantage is critical in an industry like manufacturing that faces tight deadlines and pressure to shorten lead times.

Manufacturers that use machine learning typically do so to boost efficiency, automating repetitive manual tasks. Processes like inventory audits often require several passes, so automation is an ideal alternative, but if it takes too long to develop these automated tools, it counteracts those benefits. Streamlining deployment through MLOps lets manufacturers capitalize on these tools faster.

By shortening ML development cycles, manufacturers also reduce related costs. That can help address financial barriers to machine learning adoption, helping smaller manufacturers capitalize on the technology and generate fast ROIs.

More Reliable ML Insights

MLOps can also make manufacturers’ ML models more reliable in practice. The boost in accuracy comes largely from the continuous review and testing of the data manufacturers feed into these models.

Gartner estimates poor-quality data costs businesses $12.9 million annually on average. Inaccurate data produces unreliable results, limiting machine learning’s economic feasibility in many instances. MLOps’s proactive, continuous approach to ensuring input data meets reliable quality standards ensures manufacturers’ ML models produce accurate insights.

MLOps also addresses the disconnect between data scientists and end users that interferes with meeting ML project expectations. In MLOps, developers and manufacturers work closely together, even after training an ML model. This collaboration helps create and train systems that align more closely with manufacturers’ needs and goals, making them more practical.

Enabling Flexibility

Machine learning models must also be flexible to be of practical use to manufacturers. Dramatic supply chain disruptions and demand shifts in the past few years have highlighted the need for manufacturing to become a more agile, adaptive industry. Consequently, their ML tools must be similarly able to adapt to changing processes, goals, and technologies.

According to PR Newswire, nearly three-quarters of U.S. and U.K. manufacturers have invested in new technologies amid the pandemic and almost all plan on continuing that trend. Amid that shift, an ML model designed around a manufacturer’s existing workflows and equipment won’t remain relevant for long. The facility will need an updated model and MLOps provides the agility necessary to support that change.

MLOps’ automation, shortened development cycles, and versioned models make it easy to reproduce ML models. Manufacturers can use that to their advantage by tweaking and implementing updated systems in minimal time, helping them adjust to changing circumstances.

Improved Cybersecurity

Embracing an MLOps model will also help the sector become more secure. According to IBM, as manufacturers have implemented more connected technology, they’ve become the most attacked industry in cybercrime. Because machine learning involves large data volumes, it needs more protection to address this growing risk.

The primary security advantage of MLOps is its emphasis on collaboration. With developers and end users working together from the beginning, it’s easier to understand and account for manufacturers’ unique cybersecurity needs. The continuous development pipeline also enables ongoing security audits and updates.

Reliable cybersecurity depends on the development and implementation of software. The DevSecOps principle of bringing these sides together ensures a comprehensive approach to security vulnerabilities in each stage.

MLOps Can Unlock Industry 4.0’s Potential

Machine learning is a key piece of the puzzle for capitalizing on the data manufacturers’ industry 4.0 initiatives generate. MLOps provides the path forward for taking advantage of machine learning while addressing its most common obstacles. As more manufacturers embrace MLOps, the industry can step confidently into a more data-centric future.