MLOps vs DevOps: Evolution in Operational Excellence

When software development — in the way it used to function up until the aughts — hit the wall, DevOps emerged to revolutionize the landscape. The rise of machine learning brought with it a new set of challenges, mirroring those faced by traditional software development — and that’s how MLOps came into the picture. 

Contrary to the common opinion, MLOps vs DevOps is not a study in the juxtaposition. MLOps is a natural evolution of DevOps principles and practices applied to the unique realm of machine learning. That’s why in this article, we dive into the comparison of DevOps vs MLOps with a single agenda of highlighting how MLOps can support your machine learning initiatives.

What is MLOps? Looking at the concept through the DevOps lenses

MLOps, short for machine learning operations, builds upon the foundational principles of DevOps, sharing the same goal of automating processes. But instead of optimizing software development and delivery pipeline, as DevOps does, MLOps extends beyond deploying code. It strives to automate and standardize processes across the entire ML lifecycle, allowing organizations to operationalize AI at scale. 

From data pipelines to model training and infrastructure management, MLOps tools align ML application development (Dev) with ML system deployment and operations (Ops), maximizing the value of your machine learning investments. 

Note: In the trio of AIOps vs MLOps vs DevOps, AIOps and MLOps also have different priorities. While MLOps is an extension of DevOps tailored to the machine learning domain, AIOps leverages AI and ML to automate IT operations.

DevOps walked so MLOps can run. How MLOps wins adopters’ minds

According to McKinsey, adopters of comprehensive MLOps practices shelve 30 percent fewer models and squeeze 60% more value out of their AI initiatives. These figures come off as hardly surprising, considering DevOps-inspired improvements machine learning operations usher into the AI development workflows. 

Automation and continuous processes

A hands-free approach to managing the software development life cycle is the bedrock of DevOps, powered by the implementation of automation tools that minimize human effort. Software development and operations also hinge on iterative, ongoing activities — including continuous integration (CI), continuous delivery (CD), and continuous deployment — aimed at accelerating software delivery without reducing reliability. 

MLOps takes these principles up a notch, laying them over the entire ML lifecycle. For example, automated model training kicks into action right after model training code updates or data changes, while automated testing allows AI teams to flag issues early in development and stop them in their tracks. Just like DevOps, MLOps adheres to an iterative approach where models are constantly monitored, evaluated, and refined through continuous integration, continuous delivery, continuous training, and continuous monitoring.

Version control systems and configuration management

Effective versioning of code and configuration changes are among other tenets that carry DevOps and enable development teams to collaborate effectively, perform experiment tracking, and manage code. 

In the same vein, MLOps applies version control to datasets, model code, and configurations, ensuring reproducibility, auditability, and consistency across artificial intelligence development flows.

Automated monitoring and feedback loops

DevOps is all about keeping a pulse on applications in production in case any issues come up, plus continuous monitoring helps track user interactions. 

In MLOps, continuous monitoring manifests in a slightly different way and applies mainly to model performance, data quality, and infrastructure health. Through feedback loops, AI and data science teams keep model performance in check and effortlessly spot issues like data drift, concept drift, or performance degradation.

Testing and validation

In DevOps, comprehensive testing and validation strategies revolve around automated testing, continuous integration, and continuous delivery (CI/CD) pipelines. This approach also entails a variety of testing scenarios, including unit testing, integration testing, performance testing, and other checks, integrated early in the development.

Here, MLOps follows suit, but extends testing to comprise tests for features and data, tests for model development, and tests for ML infrastructure. This holistic approach reduces the risk of deployment failures and allows engineers to tackle unique challenges like model drift and insufficient explainability.

Infrastructure scalability and flexibility

By adopting such DevOps practices as infrastructure-as-code, continuous delivery, microservices architecture, and others, companies can adapt to fluctuating workloads and rapid changes in application requirements.

Given the complexity of models and overwhelming data volumes, scalability also takes the central stage in MLOps. Along with scalability-friendly DevOps tools and practices, MLOps also employs containerization and orchestration to enable hassle-free application deployment and scaling across different environments.

Collaboration

At-scale automation and optimization of development workflows becomes a pipe dream without bridging the gaps between development and operations teams. That’s why a sync between the two is a central theme in DevOps.

MLOps expands the collaborative circle to include data scientists, ML engineers, and IT operations, streamlining the transition of machine learning models from development to production environments. Moreover, MLOps-inspired collaboration relies on model lifecycle management and governance.

Now you might think that DevOps alone can address the challenges brought about by ML workflows. But in reality, it falls short of providing a holistic ecosystem. With DevOps alone, all sides of the development process work in silos, dealing with unpredictable model experiments and manual model release processes. DevOps doesn’t imply result traceability or reproducibility so without MLOps, AI teams can’t deliver reliable, trustworthy, and compliant machine learning models.

Comparing MLOps and DevOps workflows

Both DevOps and MLOps lifecycles circulate around automated deployment, quality control, and continuous feedback — with a common goal of automating and streamlining processes. However, the paths to value are different for each approach. 

DevOps starts as early as the development environment set-up and segues into the coding stage which is then followed by the CI stage and automated testing. This lifecycle stretches as far as post-deployment monitoring, allowing development teams to enhance incident response and implement continuous improvement.

While the DevOps lifecycle consists of 8 steps, the MLOps one comprises five core stages, directly tied to model development, deployment, and management. The lifecycle unfolds with data preparation, supported by versioning, pipeline building, and data labeling. This step flows smoothly into model development and training, encompassing experiment tracking, training automation, and model versioning.  

MLOps also provides a structured approach to model deployment and post-deployment optimization, providing such automated capabilities as containerization, autoscaling, and more.

Adoption drivers for MLOps are the same for DevOps. Or are they?

While the rationale for introducing MLOps and DevOps practices may vary across organizations, common adoption drivers overlap. Both methodologies do a great job of reducing development cycle times, enhancing productivity, promoting system reliability, and last — but far from the least — fostering healthier collaborative practices. Here is a rundown of benefits that MLOps has brought to our clients’ table:

Accelerated scalability across business processes, workflows, and customer journeys

Executives often lament that the transition from AI solution idea to implementation can stretch up to over a year — and the progress remains sluggish, no matter how hefty their investments are. MLOps flips the script, allowing AI adopters to go from zero to hero in about 2 to 12 weeks with no additional talent or technical debt. 

This momentum stems from MLOps-induced standardization that is achieved through the creation of reusable components and workflow automation. Erstwhile time- and effort-consuming tasks like data ingestion, data management, and data integration become an easy lift, with little need for human oversight.

Modular pre-made components can lay the ground for creating a larger product or system — something that helped our client, a fintech company, deploy the solution five times faster and with fewer resources. By developing a central AI platform and layering modular pre-made components on top, our client rapidly adapted their recommendation engine for different countries, improving customers’ access to relevant financial products and investments.

Along with reusable model training scripts and deployment infrastructure, MLOps allows organizations to diversify their portfolio of reusable assets to include ready-to-use data products. These artifacts consolidate a particular set of data according to common standards, facilitating its repurposing for multiple current and future use cases within a specific field.

Enhanced data acquisition and preprocessing

Traditional, manual-based workflows are known for time-consuming data workflows due to inconsistent data formats, data silos, and difficulties in tracking data changes. From data loading to data transformation, automated data pipelines — aimed at extracting, transforming, and loading data efficiently — relieve data professionals of manual tasks. 

Today, data pipelines can also be augmented by an LLM, besides other traditional MLOps tools, to automate data processing activities, such as data cleaning, anomaly detection, and data summarization.

Beyond maintaining data quality, data pipelines reduce errors, facilitate governance, and, of course, reduce the time it takes to collect and process data.

Easier dataset classification and management

Whether your data engineers are enhancing data security, ensuring compliance, or buckling up for an advanced analytics solution, data classification is an essential building block for effective data management and protection. 

MLOps takes the inefficiency out of data classification and management by providing robust metadata management systems that group and tag datasets based on their source, type, and quality. Unified data repositories further facilitate consistent dataset management, making classification and timely access less of a struggle.

Moreover, MLOps processes seamlessly integrate with existing data governance frameworks, aligning data classification with the organizational policies of your company.

Closer insight into the effectiveness of dataset changes

In ML development, comparing different datasets allows data engineering teams to keep tabs on the performance of a machine learning model, spot data issues, and improve model interpretability. MLOps versioning tools facilitate tracking and managing changes made to various components of an ML solution over time. 

This deep dive comparison is accompanied by experiment tracking — the process of jotting down all experiment-related information alongside model performance metrics and hyperparameters — to reveal patterns in the interplay of different experiments. Once AI teams have singled out two models with the highest accuracy, they can run A/B testing, which is another MLOps gismo for comparing model performance with different dataset versions.

Moreover, MLOps tools provide a venue for organized experimentation, allowing development teams to easily reproduce previous runs, compare different models or configurations, and recreate experiments for verification or debugging.  

Finally, built-in analytics and visualization tools enable engineers to keep a detailed lab notebook for how changes in datasets impact model accuracy, precision, recall, and other performance metrics.

Ensured regulatory compliance at scale

While a holistic risk management strategy is non-negotiable for all machine learning projects, the practical implementation of risk management strategies hinges on the practices used by AI teams. MLOps kits out teams with ample tools for comprehensive model governance, including metadata tracking, centralized repositories, model versioning, and other trackers. 

Reusable elements, equipped with detailed documentation on their structure, use, and risk considerations, also reduce error rates and allow for seamless, uniform component updates to filter down to dependent AI solutions.

One of our fintech clients who operates in a domain with a long tradition of strict regulations contacted our MLOps team to increase the auditability of their deployed models. By implementing CI/CD integration, metadata management, and pipeline orchestration, our MLOps engineers empowered our client’s team to maintain a comprehensive audit trail of model changes and associated compliance considerations.

Improved model quality at lower costs

Automation in itself is a powerful tool for cost-friendly quality improvement, but there’s a lot more to the cost-saving potential of machine learning operations. Thanks to continuous monitoring of model performance, MLOps tools detect issues early in development and trigger automated retraining, refining model quality over time with no overhead costs. Also, by continuously monitoring and reporting resource usage, MLOps tools reveal cost-saving opportunities.

Moreover, MLOps aligns resource allocation with actual needs, reducing idle times and costs and enabling dynamic scaling, which is essential for handling traffic spikes without compromising performance or incurring additional expenses.  

It’s not DevOps vs MLOps, it’s DevOps and MLOps

While DevOps focuses on accelerating software delivery and reliability, MLOps is more about improving ML model deployment and management. But despite seemingly different objectives, these methodologies do not cancel each other out. Instead, MLOps draws upon core DevOps principles, transcending the software development field and covering the unique needs and challenges of the ML development lifecycle. 

For this very reason, pure-play DevOps, although setting up a strong foundation for software development, is not enough for environments with data as a first-class citizen, making MLOps a backbone for all things machine learning.