Would you be surprised to learn that 42 percent of the time, analytics results are not utilized by key decision-makers? This demonstrates the considerable gap — as well as the sizable opportunity —that exists between finding and using insights generated by machine learning, or ML.
“The integration of a ModelOps – or MLOps – tool is key to overcoming this gap,” says Marinela Profi, AI product strategy lead for analytics and MLOps at SAS. A key requirement to overcome this gap is a technology tool that “speaks to both data scientists and IT, reducing complexity and increasing usability,” she adds.
SAS, an Acceleration Economy Top 10 Hyperautomation Impact Enabler, was recently recognized as a leader in developing MLOps in the inaugural “IDC MarketScape: Worldwide Machine Learning Operations Platforms 2022 Vendor Assessment.” The report cited SAS Model Manager, part of the company’s SAS Viya platform, as a leading tool for enabling users to operationalize ML models.
In this analysis, we’ll explain what MLOps is, how your organization can launch an MLOps plan, and review a couple of the key MLOps tools currently available.
What Is MLOps?
Machine Learning Operations, or MLOps, encompasses AI and machine learning, or ML. MLOps, in turn, is part of a company’s overall DevOps program — which is a combination of development and IT operations.
MLOps is a set of standardized practices that focus on delivering ML models in a streamlined, efficient manner into production environments. Further, MLOps encompasses the ongoing monitoring and maintenance of the models to ensure they are in compliance with various regulatory, data, and ethics standards.
As such, ML models and MLOps should be included in your company’s global governance programs to keep business practices in line with IT practices.
Why Does It Matter?
Today, many organizations are choosing to migrate ML models from the experimental phase into operational mode. As a rising number of companies, such as SAS, make ML tools readily available, competitive stakes when it comes to strategic use of technology demand that organizations not only adopt ML models but operationalize them.
Operationalization requires an approach to the post-development process that is highly collaborative. MLOps initiatives involve various departments and team members, from data teams to IT developers and engineers to C-suite executives. The aim is to initiate a cross-company process that enables ML models to thrive in practice.
Ultimately, MLOps enables organizations to develop initiatives that are functionally sound, governed, secure, and, crucially, scalable.
How Your Organization Can Initiate MLOps
Business and tech leaders have clear-cut opportunities to launch MLOps initiatives that can be efficiently integrated into the technology stack. Two standout examples include SAS Model Manager and IBM Watson Studio.
SAS Model Manager: This centralized web app (part of the SAS Viya analytics and machine learning platform) enables users to launch, edit, and monitor analytical models. It ensures model governance and transparency, validates models to ensure high-quality predictions, and monitors performance to ensure models sustain their expected performance.
IBM Watson Studio: Part of IBM’s Cloud Pak for Data-as-a-Service, the Watson platform conducts automated AI lifecycle management. While a data team, or at least an in-house development operation, is essential for utilizing IBM Watson Studio, the results can justify the investment.
While exploring your options in the hyperautomation space, there’s a strong case to leverage ML for quantifiable business efficiencies. It’s important to recognize, however, that the production and development phase is just one part of the process. To create an actionable model, you must ensure the operational phase is assigned a high priority in your plans.
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