Accelerating ML/AI Deployments using MLOps

Photo by Amanda Dalbjörn on Unsplash

MLOps is a combination of processes, emerging best practices, and underpinning technologies that provide a scalable, centralized, and governed means to automate and scale the deployment and management of trusted ML applications in production environments. MLOps is a natural progression of DevOps in the field of ML and AI where it provides a framework with multiple features housed under a single roof. While it leverages DevOps’ focus on compliance, security, and management of IT resources, MLOps’ real emphasis is on consistent model development, deployment, and scalability.

Once a given model is deployed, it's unlikely to continue operating accurately forever. Similar to machines, models need to be continuously monitored and tracked overtime to ensure that they’re delivering value to ongoing business use cases. MLOps allows us to quickly intervene in degraded model(s), thus ensuring improved data security and enhanced model accuracy. The workflow is similar to continuous integration (CI) and continuous deployment (CD) pipelines of modern software development. MLOps provides the feature of continuous training, which is possible because models are continually evaluated for their performance accuracy and can be re-trained on new incoming datasets.

For a detailed view, check out: https://blogs.cisco.com/analytics-automation/accelerating-ml-ai-deployments-using-mlops

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https://utkarsh-00.github.io/

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Utkarsh Srivastava

Utkarsh Srivastava

https://utkarsh-00.github.io/

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