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MLOps Automation Key To Accelerating Machine Learning Projects

Integrating MLOps automation into machine learning pipelines speeds projects, reduces work, and cuts costs.

More organizations are coming to the realization that MLOps can accelerate development and maintain consistency throughout an ML projects lifecycle. Organizations without MLOps or poorly defined operations often suffer stunted development cycles due to a lack of consistent production, poor data pipeline management, and stop-start attitude towards the project lifecycle. 

The necessity of MLOps for modern day machine learning projects cannot be overstated, and automating the stages of MLOps is considered to be the next shift for leading machine learning organizations to achieve even faster development cycles. MLOps automation is integrated into the ML pipelines, reducing the amount of manual work that needs to be completed during the development process of new models. 

Enabling automation in MLOps is a three stage process, which starts with the creation of workable models. This is a manual process, in which data scientists and machine learning engineers train the model with a keen focus on adhering to MLOps practices. 

See also: MLOps vs DataOps: Will They Eventually Merge?

After the workable model is established, organizations must prioritize automated data pipelines to ensure that all data flowing into the machine learning model is formatted correctly, with the pipeline able to transform and upload the data automatically. Organizations must be aware of the triggers set up with their automated data pipelines, and routinely check to make sure the pipelines are collecting and storing data correctly. 

The third phase is the implementation of continuous integration / continuous delivery (CI/CD), which is a development practice focused on making code changes frequently and consistently. Through the automation of this process, ML models can receive continuous updates, improving the accuracy of the model.

With these automations set up, data scientists have more free time to come up with features and applications from the model, while also focusing on future improvements to model architecture. 

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