Four Things You Need To Know When Deploying Machine Learning
In a previous blog post, we covered Artificial Intelligence (AI) and machine learning and their differences. Now, we’ll discuss planning a roll-out of machine learning within your organization. Once you have determined that you are ready to deploy machine learning within your organization, there are several ways that you can strategize a more seamless introduction of this technology to your organization. Here are a few things to keep in mind as you build out your project:
1. Stakeholders. Stakeholders.
One of the most underestimated aspects of rolling out a new project or strategy is anticipating and planning for the potential obstacles and pitfalls that accompany it. By engaging those who are impacted by the new project, regardless of how minimally, you can incorporate changes during the project rather than at the end and you may discover unanticipated challenges that you can resolve prior to rollout. When stakeholders are kept abreast of the progress of the project and are allowed to provide feedback, you are more likely to have a successful deployment.
2. But what does your data look like?
Machine learning relies on optimized data that is accessible to a broad swath of teams within the organization. Instituting machine learning is a prime opportunity to review your data and consolidate and clean your data as much as possible in order to start with the best foundation. It’s imperative that you understand how much data you have, where it is located, and how you are currently using it. By performing even a minimal data audit, you can help ensure that you are deploying machine learning in the most optimal environment.
3. Dream big, start small.
Once you have determined what approach you will be taking to roll-out machine learning, you should begin your rollout by selecting a finite set of data and determining your goals. What are your organization’s expectations for machine learning? While you can plan for the future and how machine learning can help the entire organization, you must test this approach first in order to determine if machine learning will work for all aspects of your environment. A concentrated roll-out will help unearth any issues and/or opportunities.
4. Re-check. Check again.
Since machine learning is designed to help automated solutions make more informed decisions with data based on what they have been taught, not simply on tasks, it’s important that people are frequently checking to ensure that programming is functioning as it should and that the automation aspect of the roll-out is progressing as it was designed. This is why testing is so critical to a successful deployment; frequent checks allow more opportunities for feedback and enhancements. Continuous testing is key to making the most out of machine learning in a test data environment.
Machine learning is an evolving approach to bring efficiency, scalability, and speed into your environment. There are countless opportunities to deploy machine learning to help your organization continue to advance. It’s critical to carefully plan and execute your roll-out in order to meet your organization’s goals and to take advantage of this opportunity.
This post was written by:
VP of Product Development
With more than a decade of software engineering and deployment experience, Josh leads Cycle’s Product Development team, determining strategic vision and priorities for Cycle’s software offerings. In this role, he oversees Cycle’s roadmap, team development, product vision, user experience, and product culture.
Additionally, in 2009, Josh co-founded Tryon Solutions to meet the growing demand for customized, client-centered supply chain consulting. Josh brings more than a decade of experience successfully deploying enterprise-level supply chain software which was the catalyst for developing Cycle.