DevOps for AI: Make AI Operationalization a Core Business Objective!

By Veritis

DevOps for AI: Make AI Operationalization a Core Business Objective

Catalyzed by the pandemic, the speed of the shift to the cloud has moved from years to months or even days. Out of the blue, it has become imperative for every company to migrate to the cloud.

The speed, quality, scalability, and flexibility offered by the cloud have made it a haven for businesses, amid the crisis. And to buttress this rapid migration, AI implementations and intelligent automation are burgeoning.

However, here comes the trouble in paradise. How can you power up your AI delivery model? How can you integrate AI into your organization’s core business processes? Chillax! We have DevOps.

DevOps for AI

DevOps for AI

Just as DevOps brings speed, scalability, and flexibility to software development, one can leverage DevOps principles to ignite AI model delivery. When applied to AI, DevOps ensures that optimal AI delivery processes are deployed.

It enables AI to scale by integrating machine learning models from design to production. DevOps for AI facilitates flexibility and adaptability, advantages which are imperative during these times of uncertainty.

Also Read: 5 Key IAM Predictions to Watch in 2021 and Beyond!

Moreover, DevOps enables continuous delivery, deployment, and the monitoring of AI models with:

  • Speed: Accelerate time-to-market by axing non-value-added activities in AI delivery
  • Quality: Cultivate the culture of ‘fail fast and learn fast’ approach to enhance AI model quality
  • Scalability: Anticipate scalability requirements and make sure AI models can scale on demand
  • Reliability: Continuously monitor deployed AI models to keep them robust and reliable

How DevOps Helps in Operationalization of AI?

How DevOps Helps in Operationalization of AI?

Meeting the demand for AI implementations is a tough row to hoe, for the best practices around AI delivery are constantly evolving. However, this challenge can be addressed with DevOps principles. DevOps facilitates a repeatable and adaptable methodology to improve the maturity of AI delivery.

Here are four stages on how DevOps enables AI operationalization:

  • Preparing Data

As AI model accuracy depends on the quality of datasets, preparing the right datasets is a crucial step in acing AI models. A typical data preparation process includes data extraction, data filtering, data classification, and data validation. However, this is a manual and tiring task, which typically takes almost 70% of a data scientist’s time. And, here comes DevOps.

DevOps for AI automates the manual process of data preparation. It enables data pipelines to handle bulk amounts of data with ease. This ultimately improves the quality and quantity of the training datasets. Moreover, it relieves data scientists from the herculean task of data preparation and lets them focus on other crucial tasks.

  • Developing AI Model

Developing an AI model is an iterative, and time-consuming process. Typically, it involves three crucial activities: feature engineering, algorithm selection, and dataset training. Achieving an optimal model development escalates the need for multiple rounds of model training. Traditionally, this process takes place in the local workstations of data scientists, without much collaboration among various AI teams.

DevOps accelerates AI model development by facilitating the room for simultaneous development, testing and model versioning. This axes the time and effort required to achieve an optimal AI model.

  • Deploying AI Model

Many organizations experience severe headwinds while deploying an AI model in production. Typically, challenges surface when individual data scientists deploy an AI model developed in their local machines. AI models perform well in production only if they can handle a high influx of data in real-time on highly scalable and distributed platforms.

DevOps practices make AI models portable and modular, paving the path for operationalizing AI.

  • Monitoring AI Model

Typically, AI models are based on historical datasets. However, as time passes, the data and the model become obsolete. This leads to a decrease in model accuracy, resulting in ‘model drift.’

DevOps for AI enables continuous monitoring of data and model performance indicators to ensure the model stays relevant for a longer time. This enables more robust and responsive AI solutions.

Also Read: Google’s Anthos: The Hybrid and Multi-Cloud Platform That You Need

Parting Thoughts

DevOps for AI is gaining momentum as the optimal solution for organizations looking to embrace AI integration, AI-driven innovation, and intelligent automation. It brings in standardized processes from data preparation to model development to make AI operationalization a reality.

Despite clear benefits, many organizations often fail to address AI operationalization. Now is the right time to make AI operationalization a core business objective.

Reach out to learn how Veritis can help you drive your AI journey. With over a decade-long industry presence, our experts can help you assess and execute your end-to-end AI lifecycle management.

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