No one remembers when your live ML model saves time responding to customer feedback and increases profitability. Instead, the enterprise gets hit by surprise project costs and wants you to remediate immediately.

But as you know, narrowing down and fixing system components is complex. Is it that your data warehouse, integrated with your ML, is providing the ML model too many data inputs, driving up the cost? Are your ML pipelines automating the model retraining correctly? Are there too many enterprise systems accessing your ML model without your knowledge? Where do you start?

The solution lies within enterprise MLOps, an intersection of operations, AI and ML engineers, and automation. No matter how well your MLOps workflow meets today’s challenges, your business and technical ecosystem is changing moment by moment. 

This reality means your MLOps workflow and infrastructure must evolve around a finished AI product, its infrastructure, and the POC the data science teams have put forth.. To do so efficiently requires a member of your MLOps team to take responsibility for every process supporting the machine learning operations. 

The trick is to start gradually by understanding your MLOps lifecycle and best practices. Then manage these and advance your AI capabilities through the right mix of MLOps engineers and workflows.

Key Takeaways

• Implementing enterprise MLOps is an ongoing loop of MLOps stages in exploring, building, and scaling AI models.
• Five best MLOps practices ensure reliable MLOps infrastructure, including versioning, automation, monitoring, designing for reproducibility, and implementing MLOps in the enterprise according to MLOps maturity.
• Using an Ownership Map to assign roles and responsibilities is key to managing the MLOps team across the enterprise lifecycle.
• As customers evolve their AI usage, leading you to loop around the MLOps lifecycle, you need to staff the best team of MLOps engineers to meet those roles and responsibilities.

What Is Enterprise MLOps?

Enterprise MLOps takes the ML model blueprints you sketch during daily standups and implements them consistently and cost-effectively, in accordance with AI governance guidance. It manages multiple models across business teams and MLOps requirements for compliance. 

It does this by standardizing machine learning operations across the MLOps lifecycle, the backbone for a model to continuously and efficiently work in production. This advantage is critical, as 67% of corporations failed to move their AI pilots into production. Using an MLOps framework that names ML processes across the lifecycle and links accountability ensures that work reliably updates the demo ML model to work on a Tuesday at 2 am.

The Enterprise MLOps Lifecycle, Stage by Stage

For Enterprise MLOps to become a reality and make your ML predictable and reliable, it requires a stable MLOps infrastructure. The backbone of this continuous delivery hinges on the stages of the MLOps lifecycle and the MLOps maturity linked to each stage. 

• Data Ingestion and Validation: Validating data inputs for ML models is part of the exploratory phase, MLOps maturity level 0, where the MLOps teams manually extracts and analyzes the data for training preparation
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• Feature Engineering: Feature engineering cleans, transforms, and refines the training data to enhance model learning. Typically, this stage is at MLOps maturity level 0,, where the MLOps teams is refining the training data quality.
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• Model Development and Experiment Tracking: During model development and experiment tracking loops, MLOps maturity level 0, the MLOps teams chooses a model to develop.
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• Model Validation: Model validation, MLOps maturity level 1, is part of the building phase to see how the model handles unseen data. 
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• Packaging and Registry: The packaging stage, MLOps maturity level 1, puts together all of the technical components of the trained model from the registry storing the model.
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• MLOPs Deployment and Serving: Deployment and serving is the final stage in MLOps maturity level 1 which put together the developed ML model  in production.
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• MLOps Model Monitoring: The MLOps team monitors model quality, MLOps maturity level 2, to see if about updates or a redesign to scale with evolving model use in production
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• Retraining: During retraining, MLOps maturity level 2, the MLOps team improves the quality of the model outputs by helping it reason better.

AI Governance, which includes the the recommendations of the AI & GenAI Architects and Engineering Leadership, guides the MLOps team across the entire Enterprise MLOps life cycle, in what ML model to design, build and scale for the business

Enterprise MLOps Best Practices

Enterprise MLOps best practices describe how to operate the MLOps pipelines reliably throughout the MLOps lifecycle and who is responsible for those activities. There are five best practices.

• Versioning: Versioning labels model, dataset, and training script changes for tracking and debugging. ML Platform, MLOps, and AI Infrastructure Engineers own versioning.
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• Testing and CI/CD/CT MLOps Automation: The level of testing and CI/CD automation ties into the MLOps maturity. AI and GenAI Architects and Engineering Leadership own the end-to-end AI lifecycle, which includes the automation.
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• MLOps Monitoring: ML models and MLOps pipelines need to be monitored for data drift, computational performance, and quality. AI and GenAI Architects and Engineering Leadership own the monitoring strategy and lead the MLOps engineers and ML engineers to execute the MLOps processes.
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• Design for Reproducibility: When an ML model is reproducible, every phase of the MLOps lifecycle, behaves predictably in testing and CI/CD instances. ML Platform, MLOps, and AI Infrastructure engineers own reproducibility and demonstrate it in MLOps workflows.
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• Match Maturity to Organizational Capacity: MLOps in the enterprise syncs with the AI or MLOps maturity level to ensure organization resources are allocated efficiently. Succeeding at this MLOps best practice comes as AI and GenAI Architects and Engineering Leadership mentor architects, engineers, and data scientists, and create reusable AI accelerators, standards, and governance.

These MLOps principles require ownership and clearly defined roles and responsibilities. When accountability gaps exist in any of these MLOps practices, they reduce the quality of enterprise MLOps.

Who Owns Each Stage, the MLOps Ownership Map

Ensuring you have quality Enterprise MLOps requires naming an owner with accountability for each phase of the  MLOps lifecycle. That ensures optimizing automation through a standardized infrastructure and having the right type of MLOps team members in charge. Depending on the level of multi-model and business case complexity, Enterprise MLOps may require multiple ML and MLOps engineering roles on your MLOps team.

The MLOps Ownership Map

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Screening Criteria by Role

Four roles cover all eight stages above. Use this as your interview shortlist when you go to hire or vet the person behind each one.

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How To Manage an Enterprise MLOps Team

Ownership of operations in the MLOps cycle is an ongoing. Managing the Enterprise MLOps Team means matching organizational capacity to MLOps maturity. Once you standardize the AI model and its MLOps infrastructure to consistently handle that one use case, the organization wants it for something else.

MSH’s CEO, Oz Rashid notes that one of the biggest day-to-day operational gaps happens within the entire MLOps lifecycle. The customers find another great use case and say,

"Look at this. This is amazing. Look at this output. We did this." and then they won't use it that way again or they won't use it the person next to them won't use it that way. There's a lack of consistency…AI, in a lot of cases still delivers this oneoff." 

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But, we need to reproduce and deliver a reliable model for that new use case. While MLOps tools help mitigate this, the costs continue to escalate until implementing predictable machine learning operations, including continuous delivery. 

Otherwise, when customers use of the production ML models they find they degrade in performance for the existing and new use cases. This lack of model quality contributes to the cancelation of over 40% of agentic AI projects by the end of 2027.

Rather than risking restarting from scratch, management of an Enterprise MLOps Team means keeping up to date with machine learning operations and ownership of them. Where staffing needs to be flexible and aligned with budget, you need to adjust the team size to meet the maturity level. 

It’s important to take a MLOps strategic approach with a nearshore team that builds out the roles and coordinates among data science, platform, and product owners to fit the MLOps lifecycle stages. A good recruitment partner will hire high-calibre talent in record time to sync with your ML lifecycle stage while improving your cost margins.

Frequently Asked Questions

What are the stages of the MLOps lifecycle?

The MLOps lifecycle cover seven stages.

• Data Ingestion and Validation
• Feature Engineering
• Model Development and Experiment Tracking
• Packaging and Registry
• MLOps Deployment and Serving
• MLOps Model Monitoring
• Retraining

What are MLOps best practices for enterprises?

Enterprise MLOps needs to include at least five best practices.

• Versioning
• Testing and CI/CD/CT MLOps Automation
• MLOps Monitoring
• Design for Reproducibility
• Match maturity to Organizational Capacity

How do you manage an MLOps team?

Ownership of operations in the MLOps cycle is an ongoing. Managing the Enterprise MLOps Team means matching organizational capacity to MLOps maturity.

Who owns each stage of the MLOps lifecycle?

What is MLOps maturity?

MLOps maturity describes the level of automation in machine learning operations. Automated MLOps processes enable consistent ML model design, building, and scaling for production.

How do you manage model drift in production?

You manage model drift in production with Enterprise MLOps, a framework that takes the ML model blueprints you sketch during daily standups and implements them consistently and cost-effectively, in accordance with AI governance guidance. It does this by standardizing machine learning operations across the MLOps lifecycle.

Get A Consultation

Getting a handle on these surprises requires Enterprise MLOps and best practices across the MLOps lifecycle.

Key to reliable MLOps operations, throughout this loop, is an owner of that process and right sizing your team to correspond with your MLOps maturity.

Learn how to recruit, hire and onboard the right ML and MLOps engineers to keep you ML model and business operations evolving.