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Did you know that the AIs and Machine Learning field is growing significantly? This is especially true for DevOps. As our partner GitLab mentions, both AI (Artificial Intelligence) and ML are revolutionizing the way we work. Read more about it below.

The Success of AIs and ML in DevOps projects

In the last few years, there has been an explosion in the importance of artificial intelligence, machine learning, and other types of projects. Companies like Hugging Face and applications like DALL-E 2 have brought awareness to the potential AIs and ML have to improve DevOps projects.

As organizations continue learning how  to use the software as a strategic differentiator, the ability to innovate and leverage the ever-increasing amount of data companies have access to will improve and become one of the keys to improving business innovation.

However, many artificial intelligence (AI)/machine learning (ML) projects are stalled due to several challenges that even veteran software experts face. More specifically, enterprise adoption and optimization of AI/ML have been hampered by a lack of repeatable experiments, disparate tool usage, and teamwork deficiencies.

A New Model for Data Modeling

One of the primary ways to address this problem is to ensure that there is a model in place that allows a team to work out a strategic vision for AI and ML in their organization. Once that has been established, it is important to come up with a tactical “to-do list” to lay the groundwork to accomplish that vision.

From a strategic standpoint, many teams must work together to make an AI/ML program a success. First, data must be acquired and compiled into a clean, usable data set. Often referred to as “DataOps,” this encompasses all the typical ELT—extract, load, and transform—processes that data has to go through to be useful to teams.

From there, you should produce data workloads through MLOps, such as experimentation, training, testing, and deployment of meaningful models based on the data already extracted and compiled, for example. Once these two steps are complete, creating production use cases for your data is simple.

You can use applied ML to focus on improving user experience (UX), financial forecasting, or general trends and analysis of various parts of your business. Given the complexity of this value chain, knowing and understanding the history of DevOps can be key to addressing and fixing these issues.

DevOps and AIs/ML to Combat Silos

Much as the various phases of obtaining and applying AI/ML for business uses, software development consists of many teams, steps, and skills that are used in order to achieve specific corporate goals. For that reason, years ago, people came up with the concept, “DevOps,” which, by combining teams and having them work together in a continuous improvement cycle on shared goals, combats silos and inefficiencies. 

Data science teams use specialized tools that do not integrate with the software development life-cycle tools they already use. These conflicting tools cause teams to work in silos, which creates friction along with the typical lack of predictability and agility. 

Companies and software teams often fail to take advantage of the data available to them.  In turn, models take months to go into production, which may be out of date or behind competitors by the time they release. In addition, data security and ethics are often treated as secondary values, and this poses a risk to organizations, slowing innovation.

The Importance of Learning from the Past

If the past few decades of DevOps evolution have taught us anything, it is that, for companies, breaking down silos between teams through the tools and processes they use pays itself off in the long run. As your teams begin their AI/ML journey, you’ll need to consider how you can consolidate them, ensuring they work efficiently.

Therefore, maintaining an artificial intelligence/machine learning program requires improving the processes and tools your team uses. This will enable your teams to extract, compile, and load data efficiently. In addition, you will able to tune, test, and deploy models effectively, ultimately leveraging AI/ML to generate more value for stakeholders.

The DevOps culture at Epidata: Get to Know our Developments

At Epidata, we also seek to ensure established value in all our projects. For this reason, we have a DevOps team certified through Gitlab, a complete platform that guarantees constant innovation and scalability in the software creation process. This is something we apply in our developments to generate a common and consistent goal among all the work teams.

Now that you know how AIs and ML influence DevOps, we invite you to learn about some of the innovations we have implemented in our specialized area to develops all kinds of projects in less time and with less costs. Contact us or visit our website to learn more about some of our DevOps success stories. 

*References: adapted from GitLab (2022):

About Epidata

Global privately-owned company specialized in innovation outsourcing, dedicated to providing software development and software design services, application modernization, RPA, machine learning and Big Data, among others. Its solutions transform businesses, optimizing operations and co-creating better digital experiences for customers and employees.

Epidata has alliances with leading innovation and knowledge companies such as Microsoft, GitLab, Mulesoft, Salesforce, Oracle, MariaDB, Red Hat and UiPath. These partnerships help other companies to stay current.

Epidata operates in Argentina, Chile, Colombia, Peru, Uruguay and the United States (San Francisco, California), where it has a track record of successful support to multinational corporations such as Stanford Research Institute International, JP Morgan, Tenaris, Turner, Telecom, HSBC, Monsanto, Walmart, Asana, among others.