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WHAT IS MLOPS?

Machine Learning Operations (MLOps) introduces a set of collaboration and communication practices between data scientists and operations teams.

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.

 

 

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BENEFITS OF MLOPS

Savvy organizations adopt MLOps to drive innovation and improve development workflows.

 

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.

 

 

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Leverage data analytics for better decision-making

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Save time

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Reduce costs with more efficient workflows

 

MLOps can be an excellent problem-solver for injecting machine learning into development.

As a result, you can align models with business demands and regulatory compliance. The advantages your organization can realize include the following:

 

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Spurring rapid innovation

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Increasing productivity of labor with automatic pipelines and standardization of machine learning workflows

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Removing operational costs from the segment by eliminating manual work and reusing your model

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Minimizing errors through quick identification

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Ensuring reproducibility and repeatability of the model, reducing the time to deploy

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Reducing human errors to produce a more reliable pipeline

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Gaining insights about model performance through monitoring to retrain, send signals, and return high-quality information

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Standardizing machine learning workflows for more efficient collaboration, enabling more effective management of the entire lifecycle

MLOps drives seamless integration between development cycles and operations to transform how organizations handle and use big data.

CHALLENGES FACED IN THE ADOPTION OF MLOPS

MLOps has its roots in the same family as DevOps principles. The execution, however, is different.

 

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.

 

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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.

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Pipeline automation and complexity are inherent. Thus, you’ll need a multi-step process to retrain and deploy your models automatically.

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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.

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Model bias can occur, which makes it inaccurate. Instituting a feedback loop to models helps you avoid this.

 

The most complex segment of MLOps is the notable differences in continuous activities:

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Continuous Integration involves testing and validating code, components, data, data schemas, and models.

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Continuous Deployment is now an ML pipeline that should be capable of automatically deploying another service or rolling back changes from a model.

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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.

 

A hybrid team of data scientists and DevOps experts is essential to the success of MLOps. Groups can only achieve the intended goals with a foundation of cooperation and respect.

THE ROADMAP TO SUCCESS FOR MLOPS

MLOps has eight phases necessary for deployment.

1

Gather data

2

Analyze data

3

Transform or prepare data

4

Train and develop models

5

Validate models

6

Serve models

7

Monitor models

8

Retrain models