# MLOps vs DevOps: Key Differences and Why They Matter - Canonical: https://33rdsquare.com/how-is-mlops-different-from-devops/ - Published: 2024-09-03 - Author: Jordan Brown - Categories: [Artificial Intelligence & Machine Learning & ChatGPT](https://33rdsquare.com/category/tech/ai/) --- ## Introduction As machine learning (ML) becomes increasingly critical to modern software applications, the process of developing and deploying ML models is evolving rapidly. MLOps, or Machine Learning Operations, has emerged as a set of practices to reliably and efficiently develop, deploy, and maintain ML models in production. While MLOps builds on the established practices of DevOps for traditional software development, there are key differences between the two that are important to understand. In this article, we‘ll dive deep into how MLOps differs from DevOps and why a specialized approach is needed for successful ML initiatives. We‘ll cover the unique challenges of the ML lifecycle, the key components of an MLOps pipeline, the benefits it provides, and the tools and best practices for implementing MLOps in your organization. ## Traditional Software Development and DevOps To understand MLOps, let‘s first briefly review the traditional software development lifecycle and how DevOps optimizes it. In a typical process, developers write code, which then goes through building, testing, and deployment to production. Historically, these were manual, time-consuming steps performed by separate development and operations teams, leading to slow, error-prone releases. DevOps emerged as a way to streamline this process through automation and improved collaboration between teams. Key DevOps practices include: - Continuous Integration (CI): Developers frequently merge code changes into a central repository, then automatically build and test the application. This catches bugs early and ensures the codebase is always release-ready. - Continuous Delivery/Deployment (CD): Validated code changes are automatically deployed to production on a frequent basis, sometimes multiple times per day. This enables faster iteration and reduces the risk of each release. - Infrastructure as Code: Provisioning and configuration of infrastructure is defined in code and version controlled, enabling automation, reproducibility, and easier collaboration. These practices have transformed software delivery, but they are not always sufficient for machine learning projects. Let‘s look at how ML development differs fundamentally from traditional software. ## How Machine Learning Development is Different Developing machine learning applications involves additional components and complexities compared to traditional software: - Data is a key input, not just code. ML models learn patterns from training data, so the quality and composition of that data directly impacts model performance. Data can be large scale, messy, and change over time. - Models are artifacts that must be trained, tuned, versioned, and monitored. Different model types and hyperparameters can yield very different results. Models can experience drift over time as data and real-world conditions change. - ML pipelines are often more complex than traditional ETL. A typical ML pipeline may involve data ingestion, validation, preprocessing, feature engineering, training, evaluation, and deployment of model(s) – often with feedback loops and experimentations throughout. - ML testing is multifaceted. In addition to typical unit and integration tests, ML systems require testing for data quality, model performance across various data slices, potential bias/fairness issues, and more. - ML is a highly experimental, iterative process. Data scientists often conduct many experiments with different features, model types, hyperparameters, etc. Tracking these experiments and their results is crucial for reproducibility and continuous improvement. These differences mean that while DevOps provides a foundation, a tailored approach is needed to address the unique challenges of the ML lifecycle. This is where MLOps comes in. ## What is MLOps? MLOps is a set of practices that aims to deploy and maintain ML models in production reliably and efficiently. It is a collaborative function between data scientists, ML/software engineers, and operations professionals to help manage the ML lifecycle from end to end: - Data/feature engineering: Automating data ingestion, validation, preprocessing; feature store creation - Experimentation: Enabling data scientists to efficiently develop and compare models; tracking experiments - Training pipeline: Automating model training, tuning, evaluation, packaging - Model registry: Versioning and storing trained models; enabling easy deployment - Model deployment: Deploying models to production in a scalable, secure manner; supporting canary deployments, rollbacks - Model monitoring: Monitoring model performance, data quality, fairness; detecting model/data drift - Continuous training: Automating model retraining and updating in response to new data or degraded performance By considering the end-to-end ML lifecycle, MLOps enables organizations to ship ML applications quickly and reliably while improving their performance over time. It brings the agility and reliability of DevOps to the unique challenges of ML development. ## Key Components of MLOps A robust MLOps pipeline typically involves the following components: - Data pipeline: Automating the ingestion, validation, and preprocessing of training data. Ensuring data quality and detecting data drift over time. - Feature store: Enabling consistent access to precomputed features for training and inference. Versioning and monitoring features over time. - Experimentation platform: Enabling data scientists to easily develop, train, and compare models. Tracking experiments, code, hyperparameters, and results. - Model registry: Versioning, storing, and deploying trained models. Enables model lineage tracking and reproducibility. - Continuous training pipeline: Automating model retraining and updating based on new data or degraded performance. Can be triggered manually or by monitors. - Model deployment: Deploying models to production in a scalable, secure manner. Enabling canary deployments, A/B testing, and easy rollbacks. - Model monitoring: Monitoring model performance, data quality, bias, drift, etc. Alerting when issues are detected. - Infrastructure as code: Provisioning and configuring the infrastructure needed for ML pipelines in a reproducible manner. Enabling autoscaling for training and inference. ## Benefits of MLOps Adopting MLOps practices provides significant benefits to organizations: - Faster time-to-value: By automating many manual, time-consuming steps in the ML lifecycle, MLOps enables getting models to production and generating value more quickly. - Improved model performance: MLOps enables continuous training and improvement of models as new data becomes available. Models are kept fresh and accurate. - Improved reliability: With robust testing, monitoring, and the ability to quickly roll back problematic models, MLOps reduces the risk of model failures in production. - Improved scalability: MLOps practices and tools enable training and deploying models at scale across an organization. Infrastructure automation enables handling increased data and traffic. - Improved governance: MLOps provides visibility into the end-to-end ML lifecycle. It enables lineage tracking from data to models in production. This is crucial for regulatory compliance in many industries. - Improved collaboration: By adopting common tools and processes, MLOps improves handoffs and collaboration between data scientists, ML engineers, and operations teams. ## How MLOps Differs from DevOps While MLOps incorporates many DevOps practices, it extends and differs from DevOps in several key ways: - Data is a first-class citizen in MLOps, on par with code in DevOps. MLOps must consider data ingestion, validation, versioning, and monitoring in addition to code. - Models are unique artifacts in MLOps that must be versioned, stored, and monitored separately from code. Model-specific constructs like experiments, hyperparameters, and performance metrics must be tracked. - Continuous training of models is a key concept in MLOps that doesn‘t have an analog in DevOps. Models must be automatically retrained and updated as new data becomes available or performance degrades. - Testing is expanded in MLOps to include data validation, model evaluation, bias testing, etc. in addition to traditional unit/integration testing. - Monitoring considers data quality, model performance, fairness, and drift, not just application and infrastructure metrics. - ML-specific tools for data versioning, experiment tracking, model registries, and feature stores are often utilized in addition to standard DevOps tooling. So while MLOps builds on the solid foundation and mindset of DevOps, it layers on additional practices and tools to address the unique needs of the ML lifecycle. ## MLOps Tools and Platforms An extensive ecosystem of tools and platforms has emerged to support MLOps at various stages: - Data version control: DVC, Pachyderm - Experiment tracking: MLflow, Weights & Biases, Comet.ml, Neptune.ai - Feature stores: Feast, Tecton, Hopsworks - Model registries: MLflow Model Registry, SageMaker Model Registry, Azure ML Model Registry - Workflow orchestration: Airflow, Argo, Kubeflow Pipelines, Prefect - Model serving: Seldon Core, KFServing, BentoML, SageMaker, Azure ML - Model monitoring: Fiddler, Arize AI, Arthur, Alibi Detect - Full-stack ML platforms: SageMaker, Azure ML, Vertex AI, Databricks, DataRobot, Dataiku The choice of tools depends on an organization‘s current tech stack, cloud provider, and specific ML use cases and needs. ## Challenges and Best Practices Implementing MLOps presents several challenges: - Cultural shift required for greater collaboration between data science and engineering teams - Steep learning curve for data scientists to adopt software engineering best practices - Keeping up with the rapid pace of evolution in ML tooling - Governing use of sensitive data and ensuring fair, ethical AI systems Some best practices to mitigate these challenges: - Foster a collaborative culture and upskill team members in MLOps concepts - Start small, iterate quickly, and learn from failures - Choose tools that integrate well with existing infrastructure and processes - Develop robust testing and monitoring to proactively catch issues - Establish governance early and revisit regularly ## Conclusion MLOps has emerged as a set of key practices for organizations looking to scale their machine learning initiatives and generate continuous value. While it builds upon the solid foundations of DevOps, it extends and differs in key ways to address the unique challenges of the end-to-end ML lifecycle. By bringing greater automation, reproducibility, and collaboration to the ML workflow, MLOps can help organizations deliver high-quality, reliable ML applications faster. It‘s a complex, fast-evolving space that requires a significant cultural and technological shift, but the benefits are transformative. As the MLOps ecosystem continues to mature, it will be exciting to see how these practices evolve and enable all industries to harness the full potential of machine learning at scale. Embracing MLOps will be key to thriving in this new era of intelligent applications. --- Source: [MLOps vs DevOps: Key Differences and Why They Matter](https://33rdsquare.com/how-is-mlops-different-from-devops/)