MLOps in Supply Chain Management

by Dr. Phil Winder , CEO

Interos, a leading supply chain management company, partnered with Winder.AI to enhance their machine learning operations (MLOps). Together, we developed advanced MLOps technologies, including a scalable annotation system, a model deployment suite, AI templates, and a monitoring suite. This collaboration, facilitated by open-source software and Kubernetes deployments, significantly improved Interos’ AI maturity and operational efficiency.

Developing an Internal MLOps Platform

Interos relies heavily on its ability to automate the detection of disruptions in complex, highly distributed supply chains. The nature of supply chains, with their intricate networks and multiple points of potential failure, makes them susceptible to issues that can have far-reaching impacts.

The task of monitoring these supply chains involves parsing an enormous volume of information. Traditional methods of data analysis are insufficient to handle this scale of data, making machine learning the only viable automation strategy. Machine learning algorithms can sift through vast amounts of data, identify patterns, and predict potential disruptions with a level of accuracy and speed that is unattainable with manual methods.

we were rapidly scaling our entire engineering org to meet market demand following a funding round and were struggling – Hunter Powers, Vice President of Machine Learning, Interos Inc.

Constant Innovation and Experimentation

Given the dynamic nature of supply chains, Interos’ data scientists are continually experimenting with new models and techniques. This constant innovation is crucial to staying ahead of potential disruptions and ensuring the smooth operation of their clients.

However, the pace of this experimentation and, more importantly, the deployment of these new solutions into production, is directly tied to the company’s business capabilities. The faster they can test and implement these solutions, the more agile and responsive they can be to changes in the supply chain.

The Need for MLOps Expertise

Interos boasts a highly skilled MLOps platform team, responsible for managing the machine learning lifecycle, from model development to deployment and monitoring. However, as their reliance on machine learning grew, so did their backlog of tasks. The team found themselves grappling with an increasing number of models to manage, more data to process, and a growing list of experiments to run. This is where the need for additional MLOps expertise came in.

inevitably [this] would lead to unhappy customers who are not seeing the performance they expect from said models To solve our problems, we required broad skills in MLOps and DevOps, including specific expertise in Kubernetes, Kubeflow, AWS, and Argo CD. – Hunter Powers, Vice President of Machine Learning, Interos Inc.

MLOps in Supply Chain Management

Recognizing the need for additional expertise to scale their MLOps operations, Interos engaged Winder.AI in a long-term partnership. Our collaboration aimed to bolster their existing MLOps team and expedite the development and release of new features. With our specialized knowledge and dedicated MLOps developers, we were able to deliver solutions at a pace that would have been challenging for Interos to achieve themselves.

Collaborative Development of MLOps Technologies

Working closely with the Interos MLOps platform team, we embarked on an exploration and development journey across a wide range of technologies. Our collaborative efforts resulted in several key advancements:

  1. Scalable Annotation System: We developed a highly scalable annotation system to manage the vast amounts of data involved in supply chain management. This system enabled efficient labeling of data, a critical step in training accurate machine learning models.

  2. Model Deployment and Testing Suite: We created a comprehensive suite for model deployment and testing. This suite allowed for rigorous testing of new models before deployment and facilitated seamless integration of these models into production.

  3. AI Templates: We introduced AI templates to streamline the development of machine learning models. These templates provided a standardized framework for model development, leading to increased efficiency and consistency.

  4. Monitoring Suite: We implemented a monitoring suite to track the performance of deployed models. This suite provided real-time insights into model performance, enabling prompt identification and resolution of any issues.

All these solutions were deployed within a robust set of Kubernetes deployments, managed with GitOps for efficient, reliable, and scalable operations.

Commitment to Open-Source Software

A significant portion of the solutions we delivered were based on open-source projects. We believe in the power of the open-source community and its role in driving innovation. To give back to this community, we regularly committed our improvements and enhancements back to the open-source projects we utilized. This not only helped repay our debt but also contributed to the ongoing growth and development of these open-source resources.

MLOps Consulting ROI

With the support of Winder.AI, Interos was able to rapidly scale their MLOps team to meet the increasing demands of their machine learning operations. This scalability was crucial in accelerating the development process and significantly reducing their backlog. The ability to expand the team at a moment’s notice ensured that Interos could maintain a high pace of innovation and implementation, essential for their AI-driven supply chain management.

The most impressive thing about this project was how quickly could take action and deliver on goals. – Hunter Powers, Vice President of Machine Learning, Interos Inc.

Our collaboration was characterized by regular and open communication, facilitated by tools like Slack and video calls. This ensured that both teams were always aligned on objectives, progress, and any challenges encountered. The experience was not only productive but also thoroughly enjoyable, fostering a strong working relationship between the two teams.

Significant Improvement in AI Maturity

During our partnership, we witnessed a remarkable transformation in Interos’ AI maturity. They made significant strides across all aspects of their AI operations, reaching a high level of sophistication and efficiency. This maturity was reflected in their ability to develop, deploy, and manage machine learning models effectively and their capacity to leverage AI to detect and respond to supply chain disruptions swiftly.

The most unique part about working with Winder.AI was Phil’s involvement, keeping his pulse on things, and helping us with larger strategy and planning. – Hunter Powers, Vice President of Machine Learning, Interos Inc.

Our expertise and resources played a crucial role in driving these improvements. We were able to help Interos achieve these advancements on a time-scale that was significantly shorter than what would have been possible with their existing team. This not only accelerated their AI journey but also enabled them to realize the benefits of their AI investments sooner.

Start Your MLOps Project Today

In conclusion, our partnership with Interos underscores the value of MLOps consulting and development in accelerating AI maturity, enhancing operational efficiency, and driving business growth. It demonstrates how expert guidance can help businesses leverage AI technologies more effectively and efficiently, leading to significant returns on investment.

I would recommend Winder.AI to someone else because they are actual experts with real-world experience led by Phil Winder, who is well-known in the industry. They are quick to respond, quick to scale up, and deliver when you need them. 10/10. – Hunter Powers, Vice President of Machine Learning, Interos Inc.

If this work is of interest to your organization, then we’d love to talk to you. Please get in touch with the sales team at Winder.AI and we can chat about how we can help you.

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