This is the set of processes, procedures, and components that are the parts of MLOps solutions at Dysnix:
A good MLOps example stands on three pillars: data scientists, data engineers, and DevOps teamwork. Each part owns a unique set of MLOps tools and processes to be done. Thus, MLOps requires the team to be tightly integrated and interconnected to create models that work efficiently. The example of the MLOps services application at Dysnix was a case of building and deploying a model that can recognize the surgical instruments on a table using the computer vision for Explorer Surgical.
The simplest way to understand MLOps is to imagine it as a kindergarten class of robots that need to be educated on how to do their job. And all those data engineers, scientists, DevOps, AI specialists are teachers that bring all the groups of newborn robots together and raise them until they mature. Does this explain it better?
The best application of MLOps is simplifying the process of building ML models by using a mass of DevOps experience and toolkits. Starting from the environment setup and ending with correct work of deployment operations and updates, MLOps combined becomes a much more efficient model of ML model development than any other.
The best for the MLOps project is the right selection of the team. With balanced roles and distributed responsibilities, each participant in the process will know what should be done and perform it without worrying about other parts of the project. When you work with a team like Dysnix, your experts get reliable partners deep diving into the context and applying all their expertise for the sake of the project.
In a few words, to deploy ML models, you need to prepare and train them first. For this purpose, you need to prepare the training environment with all connections the production environment has. After testing, tuning, Q/A checking, and other preparations, you consider your ML model ready to deploy. You prepare a production environment and launch it there.
Training ML models is a complex of manual and automated procedures that must describe, define the architecture, set up and verify the model, and pre-set how it develops and can be updated. To produce ML models using MLOps tools, you have to clarify the goals of their work, the best architecture for them, the characteristics of environments where they might be launched, and the performance of all vital processes of the model itself.