In this article, we explain very simply what the DevOps approach is and how DevOps principles can be applied to data science.
Today, an organization must be agile and able to adapt continuously. This need for immediate adaptation resulting from increased competition and digitalization has led to an overhaul of the way companies structure themselves. When it comes to software production, the DevOps approach has already been taking precedence for several years now.
These days, the principles of the DevOps approach also tend to impose themselves when it comes to putting data science models into production. Discover what the DevOps approach is all about and how DevOps principles can be applied to data sciences.
What is DevOps?
DevOps is a method of bringing software to market that aims to integrate development teams (Dev) and software production teams (Ops). The DevOps approach suggests aligning the interests of these teams around an objective of value creation for the company and the end customer.
Using a DevOps approach implies the continuous deployment of applications through automated testing and the implementation of a culture of measurement (based on various metrics). Applications or software are therefore developed iteratively, taking into account customer feedback and test results on an ongoing basis.
The DevOps approach is therefore a dynamic approach that allows to improve its Time-to-Market (i.e. time to market) and to detect possible design errors quickly.
To summarize, the DevOps approach is based on the following fundamental principles:
- overview of the production chain, process transparency;
- collaboration between the development and production teams and alignment of their medium and long-term objectives;
- continuous product improvement;
- implementation of a culture of measurement; and
- increased automation (e.g. alert, testing, feedback systems, etc.).
Analogy between DevOps approach and traditional functions
In complex environments, the DevOps approach simply aims to reconcile the interests of the development teams and those of the software production teams from the very first stages of development.
Indeed, while the aim of the former is to accelerate time-to-market in order to preserve competitiveness, the latter must above all guarantee the stability, reliability, and security of the software.
For anyone who has worked in a large company, this is furiously reminiscent of the interactions between the sales department (sales) and the legal department (legal). For months, you've been devoting all your energy to concluding a partnership agreement to develop your business in a new territory, and now a member of the legal department tells you that the terms of the proposed contract are not suitable for X or Y reason and that you need to rethink everything. The result is unequivocal. On the one hand, you lose face with your new partner being forced to change the terms of an agreement. On the other hand, you have spent a lot of time and energy when it would have been possible to know these different imperatives beforehand.
For these reasons, many successful organizations are characterized by a strong integration of their legal department at all levels of the operational and decision-making process. The latter is seen as a partner rather than a hindrance.
Adopting the DevOps principles when implementing software means reconciling different players within the company and making them share a common goal.
While on paper such an approach may seem obvious, in practice it nevertheless involves a great deal of effort. You have to shake up the established order and devote precious time to rethinking the organizational chart and often well-established working principles. Changes must be made at the structural level but also at the cultural level.
However, the medium- to long-term benefits often justify this effort. The consulting firm BCG estimates that it usually takes between 18 and 36 months to implement the necessary changes.
Nevertheless, once the DevOps approach is adopted, the time to market will be divided by a factor of 2 to 4 depending on the company. In addition, customer satisfaction will be tripled, or even quadrupled in some cases.
How to link DevOps and Data Science?
When we know that more than two thirds of data science projects never reach production, the value of a DevOps approach in the field of data science becomes clear.
For several years now, the term DataOps has been gaining in popularity. DataOps simply aims to use Agile and other DevOps techniques to quickly transform the elements provided by the data into usable elements.
The DataOps approach encourages the collaboration of different data professions (i.e. data scientists, data engineers, data architects, business analysts, etc.) to work together towards a common goal. The fundamental idea is to integrate the business approach right from the design phase of a data science project to avoid many disappointments later on.
Following the example of the DevOps approach, one of the essential elements of DataOps techniques consists in automating all stages of the process. This element is perfectly in line with the growing desire for hyper automatization at all levels. The Gartner consulting firm considers hyper automation to be one of the major trends in the technology sector in 2020. The emphasis is therefore placed on sophisticated automation to facilitate the daily life of the company. This is exactly what DevOps or DataOps techniques are trying to achieve.
In summary, the following principles can be identified as fundamental elements of the DataOps approach:
- hyper automatization ;
- creation of open systems;
- implementation of data control and traceability systems; and
- communication and multi-directional feedback system.
These elements are only the foundation of the DataOps approach, which in practice covers a much more complex reality. For more details on the DataOps approach, we refer you to one of our previous articles: DataOps: the DevOps 2.0.
The DataOps approach and the Ryax enterprise
Adopting DevOps and DataOps techniques is currently part of good business management. The goal is to create an ever more efficient organization, with the various departments acting together to achieve a shared goal. However, because of the associated costs and the need to mobilize resources, many organizations are struggling to take this step.
Ryax offers an open data engineering platform that integrates perfectly with the DataOps approach. The Ryax platform indeed offers a common tool shared by the various actors of data sciences within the company. Ryax allows the automation of data processing chains as well as a permanent and efficient monitoring. By using Ryax, a company opens the door to dynamic DataOps techniques.
The Ryax Team.