August 29, 2022
Without a doubt, Machine Learning (ML) problem-solving tools have proven valuable for manufacturers, helping to drive everything from product improvements and identifying supply chain inefficiencies, to creating brand new revenue streams.
As the trend has gained momentum, predictive analytic tools and services have become more accessible, and ML skillsets and partners have become commonplace. Consequently, process industry manufacturers are increasingly leveraging ML to make data-driven decisions across the value chain. Some of the key areas include:
ML solutions typically benefit from experimental, iterative methods and systems during development but require certainty and supportability after deployment. While the lifecycle of a single ML solution is complex, as ML projects multiply within an organization, so too do the challenges.
For example, it’s increasingly difficult to ensure solutions are reliable, accurate and efficient in production, all while maintaining the spirit of adoption through “citizen developers,” who are devising, developing, and using the models developed. Without such assurances, ML development can stall, as well as associated business gains.
So, how can we address the apparent paradox of flexibility, accessibility and reliability desired for effective ML solutions? This where the practice of ML Operations (MLOps) comes in. Like other “Ops” categories, MLOps refers to methods and systems to manage ML models in a cohesive manner through consistent data integration, automation, governance, diagnostics and best practices.
Modern cloud technologies offer many advances to support the reliability, interoperability and maintainability of ML-based solutions.
Data scientists (and these days, that includes skilled data engineers) are trained in data analysis, ML algorithms, information visualization and focus on accuracy and usefulness of models developed. They understandably are not well-versed with (or in some cases even incentivized to care about) version control, commenting, pull requests, comprehensive unit testing and in some cases regulatory compliance issues. MLOps practices provides guidance and structure to enforce common use of data collection, semantic naming of data, ML software, coding, testing and documentation of best practices.
Smart application of MLOps tools and techniques can expand experimentation, engineering, adoption and use of industrial data science to drive business performance. Hitachi Vantara offers a practice that helps organizations set up and operate MLOps centers. A recent customer, for example, deployed a robust, but intuitive, push-of-the-button solution that automates ML training, testing and deployment. As part of the solution, we provided a playbook of best practices for the company’s data scientists and engineers to follow. On top of this, we created dashboards that provide the company a centralized diagnostic to assure that all ML executions are occurring flawlessly and accurately. In the end, however, the bigger result may be that the company now has an effective repeatable method to run models on-demand by an increased number of skilled users.
ML is here to stay in manufacturing as well as other industries. It’s imperative to start investigating, testing, and experimenting with projects as soon as possible. The value will be visible in both the short and long term.
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