MLOps: methods and tools of DevOps for machine learning

Businesses allocate up to 20% of their tech budget to AI. But where exactly does this budget go? 69% use AI for data analytics, 57% for data processing, and 47% for natural language processing, centering on data preparation or model building, rarely mentioning such an important puzzle piece as deployment. This stage consumes 25% of data scientists' time — and yet, 90% of machine learning models for data science never make it into production. 

The MLOps ecosystem emerged as a response to this problem. This article outlines the main concepts and benefits of MLOps, explains how it differs from other Ops frameworks, guides you through key MLOps phases, and introduces available tools to automate these steps. We'll also explore the key MLOps platforms and help you choose the right approach for your business.

‍

What is MLOps?

MLOps combines "machine learning" and "operations" to describe a set of practices that automate how ML models move from development to real-world use. It covers the entire journey — training models, deploying them, monitoring performance, and updating them with fresh data. The best way to describe it is machine learning model management on autopilot.

The approach bridges the gap between data scientists who build models and IT teams who run them in production. ML DevOps brings together skills from data engineering, machine learning, and traditional DevOps practices. Instead of manually moving models between environments and hoping they work, MLOps creates repeatable processes that handle the heavy lifting automatically. Why is it so important for today’s ML practices?

MLOps benefits for managing the ML lifecycle

Despite massive investments in AI, most machine learning projects often fail to deliver value. According to McKinsey, just 15% of ML projects succeed. MLOps addresses these failures by creating systematic processes. Here are the advantages they bring:

• More time for the development of new models. Traditional approaches force data scientists to handle their own deployments and infrastructure management. MLOps shifts this responsibility to operations professionals, letting data scientists focus on what they do best — building better models.
• Higher quality of predictions. Only 36% ML projects go beyond the pilot stage — often because models fail in production environments. MLOps establishes automated validation, performance evaluation, and retraining processes that catch these issues early. An MLOps engineer sets up systems that ensure models maintain their accuracy over time, protecting businesses from making decisions based on unreliable predictions.
• Shorter time to market. Only 32% of data scientists say their models usually get deployed, often due to manual deployment bottlenecks that delay releases by weeks or months. MLOps introduces automation for training, retraining, and deployment through continuous integration and delivery practices. Models move from development to production faster, reducing the time between innovation and business impact.
• Better user experience. Applications powered by stale models disappoint users with outdated predictions and recommendations. MLOps implements continuous training and monitoring that keeps models fresh and relevant. LLM integrations particularly benefit from this approach, as language models require frequent updates to maintain accuracy.

Luigi Patruno highlights another notable aspect: "There's a shocking number of what people classify as DS/ML work that can be solved in SQL. It might execute in seconds vs. the ML approach — 1 hour to train, 10 mins to validate, and the code you need to maintain." The benefits of MLOps include helping teams choose the right tool for each problem, preventing over-engineering that wastes resources and time.

When do you need MLOps?

Google defines three MLOps maturity levels. Level 0 is manual workflows for non-tech companies with rare updates. Level 1 adds automation for continuous training in changing environments. Level 2 enables full CI/CD for tech companies needing frequent, large-scale updates. 

How to define that you reached at least Level 1 — or even Level 2?  Usually, when your business reaches a tipping point where manual model management becomes impossible — specifically, in these cases.

• Your models affect critical business decisions. When ML predictions drive customer recommendations, pricing strategies, or risk assessments, downtime costs money. MLOps ensures models stay accurate and available. Continuous monitoring catches performance drops before they impact your bottom line.
• You have multiple models in production. Managing one model manually works fine. Managing five models across different environments creates chaos. Each model needs updates, monitoring, and maintenance. DevOps machine learning practices prevent this from overwhelming your team by automating deployment and monitoring processes.
• You work in regulated industries. Healthcare, finance, and insurance companies need complete audit trails for their ML systems. MLOps provides versioning for data, models, and parameters that regulators require. Every prediction becomes traceable and reproducible.
• Your data science team spends more time on infrastructure than research. If your scientists spend weeks deploying models instead of improving them, you need MLOps. The framework shifts operational burden to dedicated teams, letting researchers focus on innovation rather than server management.
• You scale beyond proof-of-concept projects. Startups often begin with simple models that one person maintains. Growth brings complexity — more data, more models, more users. MLOps creates the foundation for scaling ML operations without proportionally increasing headcount.

MLOps tools become essential once your machine learning efforts move beyond single experiments into production systems that affect real business outcomes.

‍

The difference between MLOps and other Ops

Machine learning creates operational challenges that traditional frameworks weren't designed to handle. While MLOps borrows a lot from predecessors, there are some significant distinctions.

MLOps vs DevOps

People often call MLOps "DevOps for machine learning," and the comparison makes sense given their shared automation principles. However, DevOps and machine learning operate in fundamentally different worlds that require distinct toolsets and processes.

• Code versus experiments. DevOps focuses on versioning code that behaves predictably once deployed. MLOps must track not just code, but also datasets, model parameters, and training configurations since data scientists constantly experiment with different combinations. MLOps DevOps tools need to handle this three-dimensional versioning challenge that traditional version control systems weren't built for.
• Static applications versus degrading models. DevOps applications keep consistent performance until someone changes the code. MLOps deals with models that gradually lose accuracy as real-world data shifts away from training patterns.This leads to what is known as model degradation, meaning its ability to make accurate predictions declines as time passes.
• Testing versus continuous training. DevOps runs tests to verify code works correctly before deployment. MLOps requires ongoing model retraining and validation cycles since performance monitoring often reveals the need for updates. Once degradation appears, teams must retrain models with fresh data and validate results before pushing updates to production.

The fundamental DevOps MLOps distinction lies in permanence — DevOps manages static artifacts while MLOps handles living systems that evolve with their environment.

MLOps vs DataOps

DataOps emerged around the same time as MLOps and shares DevOps automation principles, but focuses entirely on data analytics workflows, creating quite different AI use cases. The two frameworks complement each other but serve different purposes in the data-to-insight pipeline.

• Analytics versus models. DataOps handles the entire data lifecycle from collection through analysis and reporting. MLOps takes over where DataOps ends, focusing specifically on machine learning model management after data preparation completes. DataOps delivers clean datasets while MLOps turns those datasets into working prediction systems.
• Static reports versus dynamic predictions. DataOps generates historical insights and trend analysis that remain valid until new data arrives. MLOps maintains prediction engines that must respond to live data streams and adjust their behavior as patterns shift. DataOps outputs inform decisions while MLOps systems make automated decisions.
• Pipeline versus lifecycle management. DataOps automates data transformation pipelines that produce reports and dashboards for business users. MLOps requires MLOps orchestration to manage model training, deployment, and retraining cycles that adapt to changing data patterns. DataOps pipelines typically run on schedules while MLOps systems trigger based on model performance metrics.
• Quality assurance versus model validation. DataOps focuses on data quality checks, schema validation, and pipeline reliability to ensure accurate reports. MLOps adds model-specific validation including accuracy testing, bias detection, and performance monitoring that goes beyond data quality into prediction reliability.

In the essence, DataOps is the foundation that prepares high-quality data, while MLOps builds and maintains the intelligent systems that use that data to make predictions.

MLOps vs AIOps

AIOps applies artificial intelligence to optimize IT operations across entire organizations, while MLOps focuses specifically on machine learning model management. Both use AI, but for completely different purposes and audiences.

• IT operations versus model operations. AIOps analyzes system logs, monitors network performance, and detects infrastructure anomalies to keep IT systems running smoothly. MLOps manages the lifecycle of prediction models from training through deployment and monitoring. An MLOps engineer builds systems to deploy models while AIOps engineers build systems to maintain servers and networks.
• Incident response versus model updates. AIOps automatically identifies root causes of IT problems and triggers remediation workflows when servers crash or networks slow down. MLOps automatically retrains models when performance drops and validates new versions before deployment. AIOps fixes broken systems while MLOps improves working models.
• General AI tools versus machine learning platforms. AIOps uses specialized AI tools for log analysis, anomaly detection, and incident management across diverse IT environments. MLOps uses machine learning frameworks, model registries, and deployment platforms designed specifically for model workflows. AIOps tools work with any IT system while MLOps tools work with specific ML technologies.
• Broad monitoring versus focused validation. AIOps watches everything — servers, applications, databases, and networks — looking for patterns that indicate problems. MLOps concentrates on MLOps infrastructure specifically designed for model performance, data drift, and prediction accuracy. AIOps prevents system outages while MLOps prevents model degradation.

AIOps keeps your infrastructure healthy while MLOps keeps your models accurate — both essential but serving different parts of your technology stack.

‍

MLOps concepts and workflow

MLOps operates through continuous integration, delivery, and training methodologies that work together to move AI solutions from development to production faster. Understanding these core components helps see how MLOps tools create automated workflows that improve reliability.

Model training pipeline

The training pipeline automates the entire process of building ML models from raw data to validated algorithms. This pipeline ingests data, performs feature engineering, trains multiple model versions, and evaluates their performance using predefined metrics. Modern pipelines handle LLM integrations by managing the massive computational resources required for training large language models and tracking experiments across different model architectures.

The MLOps life cycle begins here with data validation checks that ensure incoming information meets quality standards before training starts. Automated testing runs throughout the pipeline to catch errors early, while version control tracks every change to data, code, and model parameters. Teams set up triggers that automatically retrain models when new data arrives or performance drops below acceptable thresholds.

Model registry

Model registry serves as a centralized repository that stores, organizes, and manages all trained models along with their metadata, performance metrics, and deployment history. In this library, every model version gets catalogued with detailed information about how it was created, what data it used, and how well it performed during testing. This enables teams to compare different model versions, rollback to previous versions when needed, and maintain complete audit trails.

An MLOps engineer uses the registry to promote models through different stages — from experimental to staging to production — with approval workflows that ensure only validated models reach live systems. The registry also tracks model lineage, showing which data and code produced each version, allowing to debug issues and reproduce successful experiments.

Model serving

Model serving involves deploying models to production environments, setting up APIs that handle prediction requests, and configuring infrastructure that scales automatically based on demand. The MLOps tech stack includes container orchestration platforms, load balancers, and monitoring systems that ensure models respond quickly and reliably.

Serving systems handle different deployment patterns like blue-green deployments, canary releases, and A/B testing that let teams gradually roll out new model versions while monitoring their impact. Infrastructure scales up during peak usage periods and scales down during quiet times, optimizing your costs. Advanced serving platforms support multi-model endpoints where different algorithms handle different types of requests within the same service.

Model monitoring

Monitoring tracks the performance and behavior of deployed ML models in production environments, watching for signs of degradation, bias, or unexpected predictions. Unlike traditional software monitoring that focuses on system uptime and error rates, model monitoring evaluates prediction accuracy, data drift, and business impact metrics that indicate whether models still solve the problems they were designed for. Advanced monitoring systems compare production data against training data, flagging when patterns shift to affect model reliability.

A machine learning operations engineer sets up dashboards that display model performance metrics, alert thresholds, and automated responses to common issues like sudden accuracy drops or unusual prediction patterns. These systems show how model performance translates into customer satisfaction, revenue impact, or operational efficiency. When monitoring detects problems, it triggers retraining pipelines, switches to backup models, or sends alerts to data science teams depending on the severity and type of issue detected.

CI/CD orchestration

CI/CD orchestration coordinates the workflow of integrating data, code, and model changes into automated pipelines that test, validate, and deploy ML systems. This orchestration layer manages dependencies between different pipeline components, ensuring that data validation completes before model training begins and that model testing finishes before deployment starts. The system handles rollback procedures when deployments fail and coordinates between multiple environments like development, staging, and production.

Here we see the DevOps vs MLOps difference most clearly — DevOps orchestration focuses on code deployment, while MLOps must manage data pipelines, model training, validation, and deployment, each with varying resource needs and timing. The system dynamically allocates compute resources, using GPUs for training, CPUs for inference, and scheduling jobs during off-peak hours to balance cost and performance.

‍

End-to-end MLOps with cloud platforms

Major cloud providers offer comprehensive MLOps platforms that handle the entire machine learning lifecycle from data preparation through model deployment and monitoring. These MLOps platforms eliminate the need to bring together separate tools by providing integrated services that work together.

Google Cloud AI Platform

Google Cloud AI platform provides a unified environment for building, training, and deploying models at scale using Google's infrastructure and expertise. The platform includes Vertex AI, which combines data engineering, model development, and MLOps capabilities into a single interface that handles everything from data labeling to model serving. Teams use AutoML for automated model building or custom training for specialized algorithms, while the platform manages underlying compute resources automatically.

The platform excels at integrating with Google's ecosystem of ML Ops tools including BigQuery for data warehousing, Cloud Storage for dataset management, and TensorFlow for model development. Built-in experiment tracking compares model performance across different configurations, while automated hyperparameter tuning optimizes model accuracy without manual intervention. The platform supports both batch and real-time predictions, scaling automatically based on demand while providing detailed monitoring and logging capabilities.

Amazon SageMaker

Amazon SageMaker offers a complete ML platform that covers data preparation, model building, training, and deployment through a collection of integrated services. The platform provides hosted Jupyter notebooks for development, managed training environments that scale to thousands of instances, and one-click deployment options that handle infrastructure provisioning automatically. SageMaker Studio creates a unified development environment where data scientists collaborate on projects while operations teams manage production deployments.

When drawing an MLOps platform comparison, SageMaker stands out for its extensive marketplace of pre-built algorithms and models that teams deploy immediately without custom development. The platform includes advanced automatic model tuning, multi-model endpoints that serve different algorithms from the same infrastructure, and built-in A/B testing capabilities for comparing model performance. SageMaker also provides monitoring through CloudWatch integration and automated model retraining workflows that maintain accuracy over time.

Microsoft Azure ML 

Microsoft Azure stands as one of the best MLOps platforms due to its deep integration with the broader Azure ecosystem and enterprise-grade governance features. The platform provides comprehensive machine learning model version control through ML registries that centralize model sharing and reuse across teams and projects. Azure ML automates the entire AI lifecycle through built-in interoperability with Azure DevOps and GitHub Actions, making it particularly attractive for organizations already using Microsoft's development tools.

The platform excels at deploying models anywhere using managed endpoints that scale across CPU and GPU machines automatically. Azure ML's governance capabilities track data lineage, set quotas, and enforce policies for compliance requirements that enterprise customers often face. The integration with MLflow provides consistent experiment tracking and artifact storage, while the registry system enables collaboration across workspaces and centralizes AI assets.

While cloud platforms offer powerful MLOps capabilities, most businesses struggle with the gap between platform features and practical implementation. COAX bridges this divide by combining AI development services with deep MLOps expertise, helping companies move from experimental models to production systems. 

Beyond ML deployment, our cloud app development services craft applications with no bloated interfaces or unnecessary complexity, just streamlined solutions that your team will genuinely want to use. We bridge ambitious AI goals and the reality of getting stuff done, whether that's deploying your first ML model or building a cloud app that combines efficiency with ease of use.

‍

Top MLOps tools

Modern machine learning operations demand sophisticated tooling that can handle the complexity of enterprise-scale deployments. The landscape of MLOps tools offers solutions that close the divide between experimental models and production-ready systems.

• lakeFS data versioning system.

Built on Git-like principles, lakeFS helps manage massive data lakes with great scalability. Unlike traditional versioning systems that struggle with large datasets, this open-source platform enables zero-copy branching for experimentation without duplicating storage resources. Businesses using lakeFS implement Write-Audit-Publish workflows through pre-commit and merge hooks, ensuring data quality remains consistent across all development stages.

• Pachyderm.

Kubernetes-native data transformation is easy with Pachyderm's approach to pipeline automation and data lineage tracking. Supporting everything from CSV files to video content, it processes data at any scale using Git-like syntax for versioning operations. The Community edition serves small teams, while enterprises benefit from advanced features that support complex multi-language workflows spanning Python, R, SQL, and C/C++ implementations.

• MLflow.

Four components make MLflow a cornerstone among best MLOps tools for managing the complete lifecycle from development through production deployment. MLflow Tracking captures every aspect of experimental runs, while MLflow Projects packages code for reproducible execution across different environments. The Model Registry provides centralized version control with sophisticated staging transitions, and MLflow Models handles deployment across diverse serving platforms including REST APIs, Apache Spark, and cloud-native solutions.

• DVC (Data Version Control).

Integrating with existing Git workflows, DVC cuts the traditional barriers between code and data versioning. This tool for MLOps enables comprehensive experiment tracking, pipeline visualization, and reproducible model development through its command-line interface. Beyond basic versioning, DVC's integration with CML (Continuous Machine Learning) creates powerful CI/CD pipelines that automatically validate model performance across different environments.

• Weights & Biases.

Interactive dashboards and artifact logging makes Weights & Biases a go-to platform for ML experiment management and collaboration. Visualization capabilities extend beyond standard metrics to create charts and panels that reveal hidden patterns in hyperparameter relationships. MLOps Databricks integration streamlines workflows by connecting with Databricks environments, while anonymous mode allows experimentation without account requirements.

• Comet ML.

Platform-agnostic Comet ML supports integration with virtually any ML library while providing sophisticated tools for model performance analysis. Rich visualizations span multiple data types including images, audio, text, and tabular formats, making it invaluable for cross-modal machine learning projects. Whether working with Scikit-learn, PyTorch, TensorFlow, or HuggingFace, teams maintain consistent experiment tracking across their entire MLOps tools list.

• Dagster.

Cloud-native orchestration takes center stage with Dagster's approach to complex data workflow management and observability. Task-based architectures combined with declarative programming models create highly testable and maintainable pipelines. Popular tool integrations enhance development experience and production reliability, making Dagster an excellent choice for teams seeking tools to deploy machine learning models at enterprise scale.

• Prefect.

Modern data orchestration reaches new heights with Prefect's intuitive approach to workflow management and monitoring across complex application ecosystems. Prefect Orion Design provides convenient Vue and TypeScript components, while Prefect Cloud enhances functionality for team collaboration and workspace management. This lightweight platform excels at creating end-to-end pipelines that adapt to changing requirements without sacrificing reliability or performance.

• Feast.

Real-time model serving becomes achievable through Feast's feature store architecture that brings together offline training and online inference. Point-in-time correctness prevents data leakage while ensuring consistent feature availability across training, validation, and production environments. Decoupling machine learning logic from underlying data infrastructure creates a unified access layer that simplifies complex data pipelines and reduces engineering overhead.

• Kedro.

Software engineering principles merge with data science through Kedro's modular architecture that emphasizes reproducibility and maintainability. Python-based workflows become more structured and collaborative, incorporating separation of concerns and version control. Dependencies, configuration management, and pipeline visualization work together to create maintainable codebases that scale effectively across distributed computing environments.

• Featureform.

Virtual feature store capabilities of Featureform allow for defining, managing, and serving ML features across collaborative environments. Built-in role-based access control ensures requirements are met automatically, while audit logs give visibility into feature usage and modifications. Dynamic serving rules adapt to changing requirements without requiring infrastructure changes, making this tool ideal for businesses with strict governance protocols.

• Deepchecks.

Open-source by design, Deepchecks validation platform changes how MLOps tools handle data integrity and model reliability. Beyond traditional testing approaches, it delivers three integrated components: custom validation suites for tabular data, NLP, and computer vision tasks, collaborative CI/CD management systems, and production monitoring capabilities. Teams can catch data drift, model degradation, and performance issues before they impact production.

• TruEra.

Advanced automated testing meets explainability in TruEra’s platform designed to elevate model quality through systematic validation and root cause analysis. What sets this solution apart from other tools for MLOps is its ability to automatically identify bias sources while providing detailed explanations for model decisions across different versions. The platform integrates with existing infrastructure, offering systematic performance testing, stability validation, and fairness assessments that help organizations maintain high standards.

‍

• Kubeflow.

Kubernetes-native ML workflows become accessible and scalable through Kubeflow’s orchestration platform that simplifies operations across diverse environments. It provides native support for TensorFlow, PyTorch, PaddlePaddle, MXNet, and XGBoost, making it one of the most versatile tools to deploy models. The centralized dashboard with interactive UI simplifies data preparation, model training, and hyperparameter tuning.

MLOps tools continue evolving to meet the demands of increasingly complex workflows. Let’s define the best practices to choose the tools you will benefit from.

How to choose the right MLOps tool

Selecting the perfect MLOps solution requires careful evaluation of your specific needs and constraints.

• Assess your requirements — find the main pain points in your current ML workflows. Do you need better data management, faster deployment, or enhanced monitoring with specialized machine learning model monitoring tools? Understanding your challenges guides the selection process to the right path.
• Evaluate how well tools work together by ensuring integration with your existing stack, including CI/CD pipelines, data warehouses, and cloud platforms. Modern top MLOps companies design their platforms with extensive API support for popular services like AWS, Azure, and Google Cloud.
• Give first consideration to scalability when choosing tools that can grow with your AI projects. Cloud-native applications provide the flexibility for expansion, supporting increasing numbers of models, growing datasets, and evolving team requirements.
• Think about open-source versus private instruments carefully, as this impacts long-term flexibility and support quality. Open-source tools like MLflow and Kubeflow offer community support and customization options, while proprietary solutions provide enterprise-grade features and dedicated support.
• Concentrate on usability: select tools that match your team's skill level and provide intuitive interfaces. Adoption may be slowed by steep learning curves, so prioritize platforms with clear documentation and comprehensive training resources.
• Governance layout ensures your MLOps stack includes applications for model governance, compliance, and ethical AI practices. Security considerations require particular attention — check our cloud security checklist to verify your chosen tools meet enterprise security standards before implementation.

Our teams at COAX specialize in seamless AI integration solutions that connect machine learning systems with existing business applications and workflows. We focus on building custom integration architectures that ensure ML models work with your current technology stack, from legacy systems to modern cloud platforms like AWS and Azure. 

COAX’s integration-first approach prioritizes practical implementation over technical complexity, turning AI investments into measurable business outcomes that reach actual customers.

‍

FAQ

What is MLOps and why do we need it?

MLOps (Machine Learning Operations) is a software engineering, data engineering, and machine learning paradigm to productionize ML systems. According to Kreuzberger (2022), MLOps bridges the development-operations gap through nine core principles: CI/CD automation, workflow orchestration, reproducibility, versioning, collaboration, continuous ML training, metadata tracking, monitoring, and feedback loops. 

What are the main advantages of open source MLOps tools over proprietary solutions?

Open source MLOps tools achieve cost-effectiveness, transparency, and social innovation. The "Employee churn prediction with MLOps" GitHub repository demonstrates the advantage through combining MLflow, Airflow, and FlaskAPI to construct end-to-end ML pipelines without licensing charges. Open source solutions enable customization flexibility, avoid vendor lock-in, achieve reproducible research, and apply cumulative developer inputs. 

How is DevOps for ML different from regular DevOps implementation?

MLOps applies DevOps to deal with the unique nature of AI. MLOps handles probabilistic ML outputs and data dependency differently than deterministic software systems, McKinsey's "Rewired" guide reports. Some of the main distinctions are: MLOps works with dynamic data-driven artifacts as opposed to static code; encompasses data preprocessing, feature engineering, and model retraining stages; requires specialized infrastructure like GPUs; involves data scientists and domain specialists beyond software teams; employs ongoing monitoring of model drift and accuracy as opposed to application performance metrics only.

How does MLOps handle model security and data privacy concerns?

MLOps security (MLSecOps), according to Calefato’s review, integrates security throughout the ML lifecycle in eight categories: authentication/authorization controls, network security with encryption, deployment automation with security testing, constant monitoring for anomaly detection, compliance with privacy regulations, secure development practices, supply chain vulnerability scanning, and security-first culture development. Key practices include zero-trust policies, adversarial training, data provenance tracking, and differential privacy implementation.

What does an MLOps engineer do daily?

According to DevOps Digest, MLOps engineers deploy, create, and run underlying infrastructure that aids the work of data science teams like feature engineering, model training, and validation. They implement ML model production pipelines, simplifying deployment processes. Routine day-to-day tasks consist of integrating software engineering skills with machine learning capabilities, encompassing programming, data science, math/statistics, problem-solving ability, ML framework ability, and hands-on prototyping ability.