Why MLOps Is Key to Deployment and Monitoring of Enterprise ML Systems and Processes


As more organizations adopt machine learning, they realize it can take months to deploy a model in production. Putting ML models into production takes a lot more than building a team of experienced data scientists or learning how to train models. About 87% of ML projects don’t get past the experiment phase and thus never make it into production. Even when models are deployed, it’s typically not at the speed or scale that will benefit the business. To meet these challenges, organizations are increasingly turning to MLOps.

MLOps refers to a methodology that applies DevOps practices to the machine learning lifecycle. From continuous integration/continuous delivery (CI/CD) and orchestration to diagnostics and governance, MLOps improves the delivery of machine learning models in the following ways:

Automating the ML Lifecycle

MLOps applies automation to the entire ML lifecycle, from model design through training and deployment. Where you are in the ML Automation Lifecycle determines how well you deploy your ML Projects. 

For most organizations, embracing automation with MLOps occurs in three stages: Manual Process, ML Pipeline Automation, and CI/CD Pipeline Automation. Organizations start with manually validating, testing, and interactively training models for subsequent automated operations. At the ML Pipeline Automation stage, continuous model training is introduced. As new data becomes available, the validation and retraining process is automatically triggered with no manual intervention. When the organization reaches the CI/CD Pipeline Automation stage, CI/CD is introduced to automatically build, test, and deploy machine learning models.

Learn More: DataRobot Unveils MLOps Solution a Centralized Hub for ML Models

Continuous Automated Monitoring

If you have deployed hundreds of models, the output your users are experiencing could be based on any of them. Hence, how would you determine which one is causing a problem?  Evaluating ML models manually is not just time-consuming; it also takes resources away from model development. 

Models must be continuously monitored and maintained over time to see how they perform and shift with new data. As models degrade, MLOps allow for faster intervention resulting in greater data security and accuracy and faster model development and deployment. MLOps’ centralized automated tracking system alerts you immediately when model performance degrades. MLOps maintains a reproducible pipeline for data preparation and training that lets you recognize and repair errors in production, letting you compare alternative models across various metrics and roll back to an older model, if necessary.

Providing Better Communication Between Production Teams and Data Scientists

Lack of coordination and improper handoff between development and operations teams lead to ML delays and errors. With development teams working in silos, an ML solution becomes a black box to those who are responsible for deploying it. 

To successfully deploy ML models, data scientists must work closely with other teams, including business, engineering and operations. MLOps’ well-established practices streamline communication, collaboration and coordination among these teams. Without MLOps processes, communication between these teams can be difficult, leading to software delays and lost productivity. MLOps shifts the emphasis of the ML lifecycle from model-centric to pipeline-centric. The pipeline becomes the product. Many MLOps tools help manage this pipeline, so ML engineers no longer need to manually use one-off scripts to prepare the data, train it, validate, and deploy it to the operations team. 

Where the model is the product of ML development, each new version of the model must be treated as a separate project. When an ML project is thought of as a pipeline, one pipeline can run multiple times to accommodate multiple versions of ever-changing, KPI-driven models. 

Learn More: Microsoft Unveils New Azure Innovations for AI and MLOps

Applying CI/CD/CT to Model Delivery

ML models are temporary and subject to change as soon as new data becomes available. As a model decays over time, it needs retraining. However, the retraining process can take as long as several months in the absence of a streamlined CI/CD process that MLOps brings in. Data scientists may not have a way of even knowing if their models will work when moved into production. The lack of a CI/CD process also makes it difficult for data scientists to track the performance of a model with fed with real-time data.  

MLOps enables seamless and timely model retraining through continuous integration (CI), continuous delivery (CD), continuous training (CT), and continuous monitoring (CM). Continuous integration for MLOps involves validating the data and the schema in addition to testing code. Continuous deployment validates the performance of models in production–and includes the ability to deploy new models and rollback changes from the model, if necessary. Continuous training involves retraining and serving models. 

The MLOps training pipeline involves the entire model preparation process starting with collecting and preparing data. Once the data is collected, validated and prepared, data scientists implement feature engineering to assign data values for training and production. Once an algorithm that defines how the model identifies data patterns is chosen, the model can start training based on historical offline data. The trained model is then evaluated and validated before being deployed through a model registry to the production pipeline. 

The production pipeline uses the deployed model to generate predictions based on online or real-world data sets. This is where the CI/CD/CT approach comes full circle through pipeline automation. The data is collected from the endpoint and enriched with additional data from the features store. This is followed by the automated processes of data preparation, model training, evaluation, validation, and eventually prediction generation. 

Learn More: Algorithmia Goes the Extra Mile for MLOps Security and Compliance

Implementing Stringent Version Control

Governance/regulatory compliance also requires that model predictions be explainable. MLOps’ stringent versioning process can tell how a model has been trained. The design, data processing, model training, deployment, and other machine learning artifacts are stored to ensure the model can be easily reproduced when provided with the same data input. 

MLOps versioning ensures that every change made to the ML model’s data sets and code base is tracked using a version control tool that tracks and saves different versions of the model, making it possible to revert to a previous version or pinpoint a bug fix when a problem arises. 

In Conclusion: ML Success Is More Than Just About Technology

Adopting MLOps practices gives you faster time-to-delivery for ML projects. However, as Michael BertholdOpens a new window , CEO of KNIME, cautions, while MLOps tools can be a huge help, there still needs to be discipline within the organization. “Monitoring/testing of models requires a clear understanding of the data biases,” said Berthold, “Scientific research on event, model change, and drift detection has most of the answers, but they are generally ignored in real life. You need to test on independent data, use challenger models and have frequent recalibration. Most data science toolboxes today totally ignore this aspect and have a very limited view on ‘end-to-end’ data science.”

Has MLOps changed the way ML systems and processes are deployed and monitored at your organization? Let us know on LinkedInOpens a new window , TwitterOpens a new window , or FacebookOpens a new window . We would love to hear from you!