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Applying these standards, you can simplify processes and automate machine learning and deep learning models in any large-scale production environment.
MLOps has three interwoven elements: machine learning, DevOps, and data engineering.
Machine learning adds value to any process or framework where it’s applied. With MLOps as part of your DevOps culture, your organization will have a foundation of collaboration between data scientists and DevOps/SRE teams.
Leverage data analytics for better decision-making
Save time
Reduce costs with more efficient workflows
As a result, you can align models with business demands and regulatory compliance. The advantages your organization can realize include the following:
Spurring rapid innovation
Increasing productivity of labor with automatic pipelines and standardization of machine learning workflows
Removing operational costs from the segment by eliminating manual work and reusing your model
Minimizing errors through quick identification
Ensuring reproducibility and repeatability of the model, reducing the time to deploy
Reducing human errors to produce a more reliable pipeline
Gaining insights about model performance through monitoring to retrain, send signals, and return high-quality information
Standardizing machine learning workflows for more efficient collaboration, enabling more effective management of the entire lifecycle
As a result, enterprises can struggle with the deployment and never make it past the prototype phase. One of the key hurdles to overcome is your people and the need to bridge the distance between data scientists and DevOps/SRE teams. Getting the people part right is crucial. You can overcome challenges and realize these benefits when you start from this premise.
Hybrid teams have different priorities: engineers, data scientists, and other stakeholders must cooperate in deploying the structure effectively. The key to this is retaining parties that are good communicators and collaborators.
Pipeline automation and complexity are inherent. Thus, you’ll need a multi-step process to retrain and deploy your models automatically.
Testing is a large part of the process beyond what occurs in DevOps, with a focus on model validation and training. Having the right tools and methods can streamline these tasks.
Model bias can occur, which makes it inaccurate. Instituting a feedback loop to models helps you avoid this.
Continuous Integration involves testing and validating code, components, data, data schemas, and models.
Continuous Deployment is now an ML pipeline that should be capable of automatically deploying another service or rolling back changes from a model.
Continuous Testing concerns the automatic retraining and serving of models.
For all these continuous processes to work, you’ll need consistent practices and a toolset that enables these tasks. If you want to make it from prototype to production, it is critical to bridge the gap between data scientists, engineers, developers, and IT personnel. Without these individuals collaborating in harmony, you’ll fail to realize the advantages of MLOps.