Why Do You Need Mlops: A New Approach To Machine Learning In Production

While digitalization is implementing the concept of Dataops, the world of Big Data introduces a new paradigm – Mlops. Read in our article what Mlops are, why business needs it and what specialists will be required when implementing practices and tools for maintaining all the operations of the Machine Learning Operations).

What is mlops, why it became relevant and what does Big Data have to do with it

By analogy with Devops and Dataops, in connection with the popularization of Machine Learning methods and the growth of their practical implementation, the business has a need to organize continuous cooperation and interaction between all participants in working with business training models to Big Data Engineers and Developers, including Data Scientist and ML specialists. The concept of Mlops is still quite young, but every day it is becoming more and more in demand. For the first time, the professional community publicly spoke about the need for a comprehensive management cycle of machine learning in industrial exploitation around 2018, after one of the Google presentations.

In practice, the problem of introducing ML models in real business is not exhausted by the preparation of data, the development and training of the neural network or another Machine Learning algorithm. The quality of Production solutions is influenced by many factors, from the verification of datasets to testing and deployment in the production environment in the form of a reliable Big Data application. This means that the real results of forecasting or classification depend not only on neural network architecture and the method of machine learning that DATA Scientist proposed, but also on how the developers team implemented this model, and the administrators unfolded it in the cluster environment. The quality of the input data (Data Quality), sources, channels and the frequency of their receipt, which refers to the field of responsibility of the Data engineer. Organizational and technical obstacles in the interaction of diverse specialists involved in the development, testing, deployment and support of ML solutions lead to an increase in the timing of the creation of the product and a decrease in its value for business.

Thus, Mlops are a culture and a set of practices of a complex and automated control of the life cycle of machine learning systems that combine their development (Development) and operational support (Operations), including Integration, testing, release, deployment and infrastructure management. We can say that Mlops expands the CRISP-DM methodology using Agile approach and technical tools of automated operations with data, ML models, code and environment. Such tools include, for example, Cloudra Data Science Workbench, which we wrote about here. It is expected that the use of Mlops in practice will avoid the common errors and problems that Data Scientist’s faced, working with CRISP-DM classic phases. We will talk about other advantages that this concept gives.

10 main advantages for business and Data Science

Of all the benefits of the implementation of Mlops, the following advantages of Agile approach are considered to be the most significant in relation to the specifics of the industrial deployment of Machine Learning:

  • Reducing the terms of obtaining qualitative results due to reliable and effective management of the life cycle of machine learning;
  • Reproducible work processes and models thanks to the methods and tools of Continous Development/Integration/Training (CI/CD/CT);
  • Ease of deployment of high-precision ML models anywhere and anytime;
  • System of complex management and continuous control of machine learning resources;
  • Elimination of organizational barriers and combining the experience of diverse ML specialists.

Thus, using Mlops, the following aspects of ML operations can be optimized:

  • Unify the cycle of release of machine learning models and software products created on their basis;
  • Automate the testing of Machine Learning artifacts, such as data checking, testing the ML model itself and its integration in the Production solution;
  • Introduce flexible principles into machine learning projects;
  • Support machine learning models and data sets for them in CI/CD/CT systems;
  • Reduce technical debt on ML models.

It is noteworthy that the organizational techniques of Mlops should be independent of the language, framework, platform and infrastructure. And from a technical point of view, the overall architecture of the Mlops system will include platforms for collecting and aggregated Big Data, application of analysis and preparation of data for ML modeling, tools for calculating and analytics, as well as tools for automated movement of the Machine Learning models, data and the software products created on their basis between different processes of their life cycle. This will allow partially or fully automate the working tasks of Data Scientist’a, Data Engineer, ML specialist, architect and developer Big Data solutions, as well as DevOPS engineer using unified and effective conveyors (Pipelines). For example, it is this idea that is implemented in GlassDoor, which we are talking about here.

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