Operationalizing AI through DevOps!

December 13, 2021

iauro Team

Contributing immensely to the global software solutions ecosystem. DevOps | Microservices | Microfrontend | DesignThinking LinkedIn

Under the influence of the pandemic, the rate of transition to the cloud has changed from a few years to months or even days. Out of the blue for every company, there was a need to move to the cloud.

Businesses in crisis have found the cloud to be a haven due to its speed, quality, scalability, and flexibility. For these rapid migrations, artificial intelligence and intelligent automation are becoming more prevalent.

However, disaster strikes in paradise. How can you improve your AI delivery model? How can you integrate AI into the core business processes of your organization? Chillax! We have DevOps.
 

DevOps for AI

Just as DevOps brings speed, scalability, and flexibility to software development, you can use DevOps principles to drive the creation of AI models. For AI, DevOps enables the deployment of optimal AI delivery processes.

It allows AI to scale by integrating machine learning models from design to production. DevOps for AI provide agility and adaptability, the benefits you need in times of uncertainty.

What’s more, DevOps enables continuous delivery, deployment, and monitoring of AI models through:

Quality: Cultivate a fail-fast-learn-fast culture to improve the quality of the AI model.

Scalability: Predict scalability requirements and ensure that AI models can scale on demand.
Speed: Accelerate time-to-market by eliminating non-value-added activities in AI delivery.
Reliability: Continuous monitoring of deployed AI models to keep them operational and reliable.

What’s the role of DevOps in AI operationalization?

Meeting the demand for AI implementations is challenging as best practices for AI are constantly evolving. However, this problem can be solved by using DevOps principles. DevOps provides a repeatable and adaptable methodology for advancing AI.
 

Here are four steps to how DevOps enables AI:

Data preparation

When working with AI models, it is critical to prepare the correct datasets since the accuracy of the model depends on it. It is common to extract data, filter data, classify data, and validate data as part of a data preparation process. Data scientists usually spend almost 70% of their time on this manual, tedious task. This is where DevOps comes in.

DevOps for AI automate the manual data preparation process. This allows data pipelines to handle large amounts of data with ease. This ultimately improves the quality and quantity of training datasets. Moreover, it saves data scientists from the gigantic task of preparing data and allows them to focus on other important tasks.
 

AI model development

Developing an AI model is an iterative and time-consuming process. Typically, it involves three critical steps: function design, algorithm selection, and training the dataset. Achieving optimal model development increases the need for multiple rounds of model training. Traditionally, this process takes place on local workstations of data scientists without much collaboration between different AI teams.

DevOps accelerates the development of AI models by facilitating the simultaneous development, testing, and versioning of models. This reduces the time and effort required to create an optimal AI model.

Deploying an AI mode

Many organizations face major hurdles when deploying an AI model in a production environment. Typically, problems arise when individual data scientists deploy an AI model developed on their local machines. Artificial intelligence models work well in a production environment only if they can handle large data flows in real-time on highly scalable and distributed platforms.
For AI to be adopted, DevOps makes AI models portable and modular.

AI model monitoring

Models of artificial intelligence are typically built on historical data. These models and the data become outdated over time. Consequently, this leads to a decrease in the accuracy of the model, which is known as “model drift”.

DevOps for Artificial Intelligence continuously monitors the data and performance metrics of a model to ensure that the model remains relevant for longer. This allows for more robust and flexible AI solutions.

Parting Thoughts

DevOps for AI is gaining traction as the optimal solution for organizations looking to embrace AI integration, AI innovation, and intelligent automation. It introduces standardized processes from data preparation to model development to make AI adoption a reality.

Despite the obvious benefits, AI adoption is often overlooked by many organizations. It’s time to make AI adoption a core business goal.

Contact us to find out how iauro can help you on your DevOps for AI journey. With over a decade of industry presence, our experts can help you assess and execute your end-to-end AI lifecycle management.

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