Artificial intelligence (AI) is transforming businesses through automated, scalable problem-solving. However, it can be a complex process to move AI models from proven concept to production-grade deployment. And the AI models only begin to add value to an organization once they are deployed and in production. So how do you maximize your return on investment (ROI) when it comes to AI in your organization?
While organizations today are committed to furthering their AI initiatives to meet business targets, many organizations get stuck in the process. The reasons range from manual or customized time-intensive development processes to too-lean experimentation to unsupportive enterprise data infrastructure to nonexistent strategies for production scale to lack of collaboration between product teams, data scientists, and operations staff. A recent study noted that 18 percent of surveyed business decision makers said it takes more than three months to deploy a new AI model into production, which is when it can deliver value back to the organization.1 Even more, only 47 percent of AI projects make it out of experimentation to be operationalized into production.2
A similar dilemma faced software development 20 years ago. The solution then was to standardize and automate application development, deployment, and management to enable IT teams to more easily release and manage software delivery and quality with efficiency. In other words, DevOps was born, marrying development with operations.
Likewise, today’s opportunity with AI has introduced machine learning operations, or MLOps, to industrialize AI model experimentation, development, and deployment at scale. MLOps can help unlock the business value of AI sooner by fast-tracking and orchestrating AI innovation, workflows, and production at scale. MLOps also supports AI production by monitoring quality, managing regulatory requirements, and ensuring continuous improvement processes are in place.
Infrastructure Is the Foundation for AI and MLOps
While MLOps can help industrialize your AI for production to maximize your ROI, there are best practices to get the most from MLOps. Operations, in this case, means considering potential issues in your technology and critical infrastructure to ensure smooth, secure operations.
The technical foundation for AI has two parts. The first is the technology that can gather and synthesize data from across the organization, from workstation to edge to cloud. The second is the technology that allows the value of that data analysis to be unlocked by business users for insights in near-real time.
Red flags to look for in your technology ecosystem include:
- Latency in processing, which could indicate a need for additional compute power
- Insufficient memory and data storage capabilities, which may be resolved by one or more cloud technologies
- Cybersecurity gaps, which can be improved with hardware-based solutions
There are powerful tools from software vendors and the open source community available today—including from cnvrg.io, C3.ai, Databricks, and SAS—that offer such open source tools as Kubeflow, Metaflow, Kedro, and MLflow. These tools have low-code options in their data workflow that help ease AI adoption and increase speed to production. For example, cnvrg.io from Intel can help operationalize AI faster while ensuring the right infrastructure is in place.
Whatever your industry and current journey with AI knowledge and projects, you can maximize your ROI by working with an MLOps approach to industrialize your AI initiatives and ensure your technology infrastructure has the optimal foundation to generate value as quickly as possible.
1. “2020 state of enterprise machine learning,” Algorithmia, 2019, https://info.algorithmia.com/hubfs/2019/Whitepapers/The-State-of-Enterprise-ML-2020/Algorithmia_2020_State_of_Enterprise_ML.pdf.
2. Jessica Davis, “Getting Machine Learning into Production: MLOps,” InformationWeek, June 26, 2019, https://www.informationweek.com/ai-or-machine-learning/getting-machine-learning-into-production-mlops.
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