MLOps Graduates to Enterprise Model Management (and what that means for global enterprise)

In this special guest feature, Mrinal Chakraborty, DISC Solution Leader at Pactera EDGE, discusses six core aspects of MLOps which are augmenting Enterprise Model Management.

Pactera EDGE is a leading global digital solution provider for data-driven, intelligent enterprises.

Most of us see the development of machine learning models as still being very much in the nascent stages.

It’s far from being an exact science, there’s a lot of trial-and-error, and baby-steps forward are invariably countered giant-steps back.

That said, progress is still being made.

The MLOps market is very much on target to exceed $4 billion globally, and with numbers like that, MLOps is posed to a be a major component of the AI solution landscape in pretty short order.

Watch for enterprise-wide adoption sooner than later.

But how has this come about? googletag.


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display(div-gpt-ad-1439400881943-0); }); From the beginning, MLOps has tried to define the processes that make up reliable ML development and deployment.

MLOps has worked to curate the best practices that make ML development more dependable and efficient, and in doing so, it’s actually evolved into something much bigger.

Intentionally or not, it’s actually evolved into an independent approach for Machine Learning lifecycle management.

The practical core aspects of MLOps necessarily focus on the lifecycle of model development and associated aspects of machine learning model operationalization.

Here are the six core aspects of MLOps which are augmenting Enterprise Model Management: Model Lifecycle Management for Scaling Enterprise-grade Adoption – Similar to the needs for application development processes in traditional “DevOps” methodology, MLOps methodology helps to manage the lifecycle for model development, training, deployment, and operationalization.

It’s predominantly pivoted to provide consistent processes for moving models from the data science environment to the production environment.

Model Versioning & Data Realization – As models are built, they will most likely be iterated and versioned to deal with the nuances of data and iterative engineering.

 Machine Learning models that perform well in theory, may change based on new training or real-world data.

MLOps can operationalize this whole workflow by providing solutions for different versions of models, supporting multiple versions in operation as needed, provide notification to model users of version changes, visibility into model version history, and can help make sure that obsolete models are systemically flushed out.

Model Monitoring and Management – Continuous training is done to get the model from ‘Sample’ data to ‘Real’ data.

MLOps solutions need to monitor and manage model usage while this transitioning of data realization.

MLOps traces the consumption and results of models to make sure that their accuracy and performance continue to provide acceptable results.

This is much needed for visibility into data and prevent model “drift,” while keeping an eye on various measures of model performance against thresholds and benchmarks.

Model Governance – Models that are used for enterprise consumption needs to be tied to business outcomes.

As such, MLOps platforms provide auditing, compliance, governance, and access control through the entire process.

This includes features for model and data audit-trails (tracing data changes to model change), model access control, prioritizing model access.

Its ensures to provide transparency into how models use data, and any regulatory or compliance needs for model usage.

Model Discovery and Parameterization – While the enterprise matures in advanced MLOps adoption, the MLOps methodology may also provide model registries (and parameterization templates) or catalogs for models produced within the development ecosystem.

 A searchable intra-enterprise-marketplace can provide a way to locate consumable models, both internally developed as well as third-party models.

This capability for model discovery should enable users to ascertain the relevance, quality, data origination, transparency of model generation, and other factors for a particular model.

Model Security with Cloud Security Features – Machine Learning models are the IP that need to be protected as enterprise data-assets.

MLOps solutions, borrowing from its cloud-native security features, can provide the functionality to protect models from being corrupted by un-reliable data, DOS (denial of service attacks) and Role-based access.

  As MLOps start building on a promise to deliver solutions that Data Science and IT Ops teams need to work together to deploy, monitor, manage, and govern ML/AI models in production, global brands will look to enterprise-grade models.

With services across the full spectrum of the ML lifecycle, MLOps can be leveraged, using a systemic approach, to build, train, and deploy ML models, and ensure those models continue delivering value — without the company needing to necessarily build its own team of ML/AI specialists.

 We’re beginning to see that future, now.

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