Accelerating AI Innovation: The Power of DevOps Principles

Artificial Intelligence (AI) is becoming increasingly popular, and the next wave of competitive advantage is expected to come from emerging technologies such as blockchain, the cloud, and AI. This has led to a rapid shift towards digital technologies, which has gone from years to months or even days. Consequently, there has been a significant increase in the number of intelligent automation and AI implementations.

To integrate AI into an organization’s DNA, DevOps principles for AI are essential. DevOps principles enable AI at scale by operationalizing machine learning models from design to production. DevOps for AI ensures that the right AI delivery processes are in place, bringing flexibility and a “fail fast” approach needed during times of constant change and technological transformation. Essentially, DevOps for AI facilitates continuous delivery, deployment, and monitoring of models.

DevOps for AI offers several benefits, including speeding up time-to-market by reducing non-value-added activities in AI delivery, accelerating cleaning datasets, and promoting continuous learning to improve AI model quality. Additionally, DevOps for AI ensures that AI models can scale on-demand and are reliable, stable, and accurate.

Applying best practices to AI model operationalization is crucial for meeting the growing demand for AI implementations. DevOps principles provide a repeatable and adaptable approach to increase the maturity of AI delivery. The four stages of AI operationalization include data preparation, AI model development, AI model deployment, and AI model monitoring and continuous learning.

The data preparation stage is a crucial starting step in AI operationalization, as model accuracy depends on the quality and size of the training dataset. Traditionally, data preparation is a manual and cumbersome task where data scientists typically spend around 70% of their time. DevOps for AI automates such steps and enables data pipelines to handle big data, improving the quality and size of datasets.

The AI model development stage comprises three main activities – feature engineering, algorithm selection, and dataset training. Model development is an iterative process that requires multiple rounds of model training to arrive at an optimal solution. DevOps practices speed up AI model development by providing elastic infrastructure and the processes for parallel development, parallel testing, and model versioning, reducing the time and effort required to arrive at the optimal model.

Deploying an AI model in production is an extremely challenging area for many organizations, and DevOps methods make AI models portable and modular. Such architecture allows AI at scale in operationalizing AI. Once deployed, models face the threat of “model drift,” where the models deployed in production become outdated, resulting in a decrease in model accuracy. DevOps for AI brings in the concept of continuous learning, where data and model performance indicators such as drift and accuracy, are monitored to ensure they stay relevant for a longer time.

In conclusion, DevOps for AI is a promising solution for companies looking to accelerate AI-driven innovation and intelligent automation. It brings in standardized processes to make AI at scale a reality, making AI operationalization a core business objective. Companies can apply DevOps principles to their AI delivery processes, bringing flexibility and a “fail fast” approach to these times of constant change and technological transformation. By operationalizing AI models using DevOps practices, companies can gain a competitive advantage and deliver powerful change that improves productivity, efficiency, and business impact.