A welding robotics manufacturer had an internal Computer Vision Data Science team manually handle processes without a standard process for automating model training, deployment, and monitoring. The team was growing to include 15 data scientists. As the team scaled, they faced a variety of tools, processes, and development approaches to increase the modeling performance and throughput. The goal was to enable them to build, train, test, deploy, and track machine learning models faster and more efficiently using an automated MLOps platform.
The manufacturer leveraged AlignAI’s MLOps accelerator approach to establish and expedite the capability via specific technology, organization, and the processes framework. AlignAI helped the manufacturer implement a scalable MLOps platform to automate the model life cycle with minimal overhead & management.
Solution process:
With AlignAI, the manufacturer has:
With AlignAI, the welding robotics manufacturer now has an organizational design for the MLOps team and processes. Through using AlignAI, the MLOps capabilities have established and implemented short-term opportunities with a long-term roadmap. Now, the MLOps team has reconnected inputs and outputs between other business groups to bring clarity across departments and enhance the overall workflow of the organization. Additionally, by utilizing AlignAI, the organization has successfully automated pipelines, automated model lineage, and metadata capture to reduce the workload on data scientists. Finally, the welding robotics manufacturer has implemented an AlignAI MLOps platform process to simplify management of the models, MLOps pipelines, and output for the company.
The diagram above reflects the team that was created and trained to map out the functions of MLOps at this robotics company.
The diagram above shows the architecture AlignAI designed and implemented with this robotics company focused on continuous improvement and training automation.