5 Key Benefits of MLOps Practices for Businesses in 2025

MLOps is a method based on adapting DevOps practices to machine learning development processes. MLOps is useful in transitioning from running a couple of ML models manually to using ML models in the entire company operation. 

Overall, MLOps helps you improve delivery time, reduce defects, and make data science more productive. We’ll explain key points on how MLOps can benefit your company’s workflow.

1. Productivity

MLOps increases the productivity of all processes within the ML lifecycle by:

Creating automated pipelines

There are many labor-intensive and repetitive tasks within the ML lifecycle. For instance, data scientists spent nearly halfof their time getting the data ready for the model. Manual data collection and preparation are inefficient and can lead to suboptimal results.

MLOps stands for automating the entire workflow of the ML model. This covers all the actions from data collection to model development, testing, retraining, and deployment. MLOps practices save time for teams and prevent human-induced errors. In this way, teams can engage in more value-added efforts rather than repetitive tasks.

Standardizing ML workflows for efficient collaboration

Company-wide adoption of ML models requires collaboration between not just data scientists and engineers but also IT and business professionals. MLOps practices enable businesses to standardize the ML workflows and create a common language for all stakeholders. This minimizes incompatibility issues and speeds up the entire process from creation to deployment of models.

Real-life example 

Netflix uses MLOps extensively to manage its recommendation system, which personalizes content for users. The company developed an internal tool called Metaflow, which automates the entire machine learning workflow—from data preprocessing to model deployment. By streamlining these processes, Netflix can deploy models faster and maintain consistency across its vast microservices architecture. This has allowed Netflix to continuously update recommendations and offer personalized content experiences at scale

2. Reproducibility

Automating ML workflows provides reproducibility and repeatability in many aspects, including how ML models are trained, evaluated, and deployed. This makes continuously trained models dynamic and integrated into change:

• Data versioning: MLOps ensures storing different versions of data that were created or changed at specific points in time and saving snapshots of different versions of data sets.
• Model versioning: MLOps practices involve creating feature stores for different types of model features and versioning the model with different hyperparameters and model types.

Real-life example

Airbnb uses machine learning models to predict optimal rental pricing, incorporating features like location, demand, and time of year. By implementing MLOps practices, they were able to manage data and model versioning effectively, ensuring reproducibility. This allows Airbnb to track changes in data over time and re-evaluate model performance using historical datasets. This system of versioning helps ensure that pricing models can be reproduced and fine-tuned, improving both accuracy and compliance with changing market conditions​

3. Reliability

By incorporating CI/CD principles from DevOps into the machine learning processes, MLOps makes ML pipelines more reliable. Automated ML lifecycle minimizes human errors and companies gain realistic data and insights. 

One of the biggest challenges of ML development is scaling from a small model to a large production system. MLOps streamlines model management processes to enable reliable scaling.

Real-life example

Microsoft uses MLOps practices for scaling its AI models across its Azure platform. One well-documented challenge in the AI lifecycle is taking machine learning models from a small-scale, experimental environment to a full-scale production system capable of handling large amounts of data and complex workflows. Through its Azure Machine Learning service, Microsoft has implemented CI/CD principles in its MLOps pipelines to automate the entire process, from data preparation to model deployment.

This MLOps approach minimizes manual intervention, reducing human error and increasing the reliability of the models. Microsoft ensures that new models and updates can be quickly and safely integrated into their services, such as recommendation engines and other AI-based applications, while maintaining consistent performance and reliability at scale.

4. Monitorability

Monitoring the behavior and performance of ML models is essential because models drift over time as the environment changes.  MLOps enable businesses to monitor and get insights about model performance systematically by: 

• Retraining the model continuously: ML models are monitored and automatically retrained periodically or after a certain event. The purpose of model retraining is to ensure that it consistently provides the most accurate output.
• Sending automated alerts to staff in case of model drift: MLOps gives the business real-time status of your data and model and alerts the relevant employees if the model performance degrades below a certain threshold. Thus, it enables you to take quick actions against model degradation.

Real-life example

A real-life example of MLOps applied to monitoring and retraining can be found in Amazon’s fraud detection systemusing Amazon SageMaker. Amazon’s fraud detection models are deployed to monitor transactions in real-time. Over time, the underlying patterns of fraud can evolve, leading to model drift—a situation where the model’s accuracy decreases as the data it operates on changes from what it was trained on.

To manage this, Amazon uses MLOps to continuously monitor the model’s performance and detect data drift. When the model’s performance metrics, such as accuracy or F1 score, fall below a certain threshold, SageMaker automatically triggers an alert and initiates retraining of the model with updated data. This process is automated through the use of CI/CD pipelines, allowing for quick model retraining and redeployment without significant manual intervention. This ensures that the fraud detection model remains effective in identifying suspicious activity, even as fraud tactics change over time​

5. Cost Reduction

MLOps can significantly reduce costs over the entire machine learning lifecycle:

• Automation minimizes the manual efforts to manage machine learning models. This will free up employee time which can be used for more productive tasks.
• It enables you to detect and reduce errors more systematically. Decreased errors during model management will also translate to reduced costs. 

Real-life example

A real-life example of cost reduction through MLOps can be seen in Ntropy, a company that provides infrastructure for machine learning workloads. Ntropy faced challenges with managing idle instances and high infrastructure costs. Initially, they relied on Amazon EC2 instances for training machine learning models but found that these instances were underutilized 75% of the time, leading to excessive costs.

To address this, Ntropy implemented MLOps practices by leveraging tools like Kubeflow and Linode, and later, preemptible A100 nodes on Google Cloud. By streamlining the orchestration and management of their ML infrastructure, Ntropy was able to optimize GPU usage, scale their resources efficiently, and implement automation for various workflows like training and deployment. This resulted in an 8x reduction in infrastructure costs, while also achieving faster model training times—up to 4x faster than their original setup​.

If you want to get started with MLOps in your business, you can check our data-driven list of MLOps platforms. Be sure to check AI Governance tools as they encompass MLOps.