The MLOps is an adaptation of Agile practices to the world of data on the way to production. It is therefore one more initiative around the issue of data. The MLOps starts where the Proof Of Concept ends. The goal is to improve the life cycle of datascience projects. This is what the MLOps is.
Faster project completion
MLOps is a practice of collaboration and communication between data scientists and members of the company assigned to operations to manage the ML production lifecycle. This practice aims to increase automation while improving the quality of ML production. Business and regulatory requirements must be considered throughout the process.
The MLOps were initially only a set of best practices. They have evolved into an independent approach to LCM lifecycle management. The MLOps applies to the entire lifecycle and business metrics.
The MLOps is increasingly needed in the field of data science as well as in data analysis. Projects are completed faster and more comprehensively. As a result, oriented projects are carried out with a higher level of service. The adoption of the MLOps represents an important change in practice that needs to be adapted to. The practice implies the adoption of the culture of continuous improvement.
Artificial intelligence is more and more often introduced in the loop by companies. However, it is important to have good expertise. It is difficult to rely on a good organizational architecture. You must find the right people to form the team that will know how to use the technology. The challenge is to combine these elements to optimize the workflow.
The change in mentality is important because it is in line with the use of new technologies and the contribution of the data scientist and the data engineer. The production and the job must also be solicited, which requires an agreement between all parties involved.
Several practices related to DevOps are present in the MLOps, especially regarding automation, unit testing and version management. Monitoring and service scalability are also related aspects of the MLOps. Some aspects are also related to data science, such as reproducibility and model performance monitoring. Model provisioning is also part of data science.
Maintaining the predictive model
Once the predictive model is implemented, it is essential to maintain it and resolve faults and breakdowns. The production team alone cannot predict when repairs will be necessary. The organization of the company must be distributed according to the new issues. It is essential to set up governance around projects to properly distribute responsibilities.
The players involved must be placed in the loop from the start of the project. The data scientist must be able to quickly find the data needed for his project. He must be able to examine it. He must also be able to identify what he can and cannot use. Once a high level of service is reached, more and more use cases must be found. It is then time to proceed with adoption for the actors involved in the project.
Involvement of the teams
The involvement of several teams is necessary in MLOps. The difficulty is to connect teams that are not used to working together. Dedicated working methods must be created.
Machine learning requires a sharp supervision. Several elements must be controlled. It is necessary to supervise the state of the system. The load level and availability of the system must be monitored. Special attention must also be paid to the incoming data. Their consistency over time must be ensured. Supervisors must consider the fact that updating influences the way the data is retrieved.
If data changes, the prediction will no longer be the same. The results may then be different from those obtained in the tests. Predictions can also influence the recovered data. Supervision is then necessary to prevent a series of errors from being triggered.
Large scale production
The speed obtained thanks to MLOps makes it possible to organize large-scale production. The steps are like software development. The execution of ML models is very similar. Therefore, we talk about MLOps, i.e., ML + Ops. It is therefore the merging of machine learning processes with the DevOps workflow.
The repeated action of ML involves the same elements as software development. The MLOps is characterized by all stages of ML deployment. The MLOps can undergo all the necessary iterations. Tests and errors can be repeated until the right set of parameters is found. It will then become possible to obtain reproducible models. In the DevOps, there would be reliable pre-established configurations.
It is also important to note that updating data causes changes in performance. When the model changes, the pipeline changes as well. The generated model must be followed to trace the origin of a possible problem should it occur. With MLOps, before starting a build, several steps need to be automated.
In this area, it is also important that team members come from a variety of backgrounds. Several trades can be integrated into the team. Businesspeople and software engineers are often called upon to be part of the development team.
Implementing the MLOps
The implementation of MLOps cannot be done lightly. The stakes are high. An in-depth knowledge of the company's objectives is necessary to identify how the MLOps is relevant. The composition of the team is important as well as the organizational architecture. Both the data scientists and the IT team need to contribute.
Several technologies are used in MLOps. These include the Elk, Kubernetes, Docker, and Rancher test. The Elk test is primarily used to determine the extent to which a vehicle escapes a suddenly occurring obstacle. As for Kubernetes, it is an open-source system that aims to provide a platform that supports the implementation of application containers on server clusters.
The optimized workflow involves migration testing, model validation and model training, in addition to some tuning.
Tools to create
Several tools are developed to meet the needs of the MLOps. These tools are likely to change frequently since this element is of recent creation. A tool such as Metaflow gives data scientists the means to facilitate the integration of a framework while continuing to create complex models. It can be used in conjunction with its data science library. The R language is also supported.
This tool was originally developed at Netflix to meet the needs of its own data scientists. They needed it in their data science work.
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La Ryax Team.