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DevOps and Data Science: How Can They Work Together

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DevOps and Data Science: How Can They Work Together

DevOps and Data Science: How Can They Work Together
date_range - 2 years ago

The combination of DevOps with Data Science is known as DataDevOps, or simply DataOps. The goal is to provide Data Scientists with the tools they need to create dependable software. The disadvantage is that it forces them to become experts at everything.

The value of applications has shifted dramatically as a result of the desire to be customer-centric. Applications must be deployed quickly, in accordance with user requirements, and in an agile manner to react to a rapidly changing environment. Similarly, the value of data has grown multi-fold as well. To make customer satisfaction decisions and gain insights, firms must now source, tap, distill, analyze, interpret, and apply data.

This reflects the present business landscape's increasing adoption of DevOps and Data Science.

Rapid development

DevOps is becoming a major disruptive wave for many businesses, with an estimated market value of $17 billion by 2026. On the other hand, the Data Science market is expected to develop at an exponential rate from $37.9 billion in 2019 to $140.9 billion in 2024. Both of these new technology fields have had a quick and impressive growth in recent years.

Only 13% of Data Science projects make it into production, according to statistics. This chasm is caused by a lack of speed and collaboration. As a result, a DevOps approach can greatly aid in the empowerment of data scientists. It puts them in contact with data engineers, user stewards, analysts, business intelligence professionals, and actual business decision-makers.

Data scientists are using DevOps principles to hone their skills by:

  • Adding flexibility and transparency to the deployment of production models
  • Handling infrastructure management as an important aspect of model development
  • Reducing model deployment time and resource consumption
  • Concentrating on the design issue rather than the infrastructural issue
  • Considering factors like scale, downtime, economics, and efficiency while deploying models
  • Using APIs, online interfaces, and client-consumption platforms to train and export models
  • Taking Data Science models and turning them into useful applications
  • Using a ‘try/fail quick but stay steady’ mindset to balance experimentation with the stability of models and algorithms.
  • Establishing ownership and clarity at every level of the model cycle

This method is assisting data scientists in reconsidering how they structure the entire spectrum. As DevOps engineers develop and test applications, they are pushed to embrace cooperation, clarity, agility, and continuous improvement.

Slower utilization

Most firms are still striving to reach maturity and outcomes from AI, machine learning, and algorithm. According to Anaconda's report, The State of Data Science 2020, professionals spend 45 percent of their time getting data ready (loading and cleansing) before they can utilize it to construct models and visualizations.

Even once the models are ready for production, issues arise in a variety of settings, dependencies, and skill shortages. Models' capacity to detect the last stage of actual exposure and impact is hampered as a result. These production issues also explain why just 48% of respondents believed they could demonstrate the impact of Data Science on business outcomes.


DevOps has fundamentally changed the way many businesses develop and manage their applications. The success of Data Science models requires a strong focus on cooperation, agility, and customer-centricity. Models aren't supposed to be trapped in boxes. This lid is readily opened by DevOps.

This is the current engineering conundrum. Everyone aspires to be a fabled 'Full-Stack Engineer,' but no one ever will. Simply because the goalposts in an already vast body of knowledge are continuously shifting. Do you want to know more?

If you want to know more about DevOps in general or you need help:


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