2 years ago

Category: Software, Business, Data, Microsoft, Kubernetes, Docker, Jenkins, gitlab, automation, machine-learning, artificial-intelligence

Of the four key points mentioned, especially for commercial ML solution providers, an erroneous development process happens to be more defiling to the maturity of a data science or machine learning project from data sourcing through production. Commercial ML solution providers also face serious issues with the process of moving to the cloud, creating and managing ML pipelines at scale, deployment, and automation of model development workflows, and making the ML solution available to a large number of users.

In addition to that, MLOps tests machine learning systems by training and validating the model at each of the data, ML model, and application pipelines to ensure that the system is reliable and validate that the feature is useful when it eventually gets to production.

To safely implement MLOps principles and reach the full business potential of machine learning, the entire model development workflow is grouped into 3 iterative phases — design, model development, and operations — with MLOps principles that streamline each process.

MLOps helps organizations create an automated workflow that is repeatable and error-resilient to facilitate cross-team collaboration, compliance with regulatory ethics, overcome prevalent challenges sabotaging the maturity of machine learning projects to production, and leverage the capabilities of the ML model to drive business growth.