AI MLOPS Masters

Menu
Whatsapp Us

Top 10 Key Differences MLOps vs Traditional DevOps

MLOps vs Traditional DevOps

Introduction

In the modern tech world, MLOps (Machine Learning Operations) and DevOps (Development Operations) are two powerful methodologies that drive automation, efficiency, and reliability in software and AI development. While both aim to streamline workflows, their focus, tools, and outcomes differ significantly.

DevOps is built around software — it manages code development, testing, deployment, and monitoring to ensure fast and reliable application delivery. In contrast, MLOps is designed for the machine learning lifecycle, managing data pipelines, model training, evaluation, deployment, and retraining.

Here are the Top 10 Key Differences MLOps vs Traditional DevOps   

  1. Purpose and Focus: DevOps handles software delivery, while MLOps manages AI/ML model lifecycles.
  2. Lifecycle Management: DevOps follows a linear CI/CD pipeline; MLOps adds data and retraining loops.
  3. Data Dependency: DevOps is code-centric, but MLOps is highly data-centric.
  4. Tooling and Technologies: DevOps uses Jenkins, Docker, and Git; MLOps adds MLflow, Kubeflow, and DVC.
  5. Deployment Complexity: DevOps deploys apps, while MLOps deploys constantly evolving ML models.
  6. Monitoring and Logging: DevOps monitors system health; MLOps also tracks model accuracy and data drift.
  7. Collaboration Between Teams: DevOps connects developers and operations; MLOps adds data scientists and ML engineers.
  8. Version Control: DevOps versions code; MLOps versions code, data, and models.
  9. Automation: DevOps automates CI/CD; MLOps automates data pipelines, model training, and retraining.
  10. Goal and Outcome: DevOps ensures reliable software delivery; MLOps ensures continuous, high-quality AI predictions.

In short, DevOps builds and maintains software, while MLOps builds, maintains, and improves machine learning models turning AI systems into real, measurable business value.

1. Purpose and Focus

DevOps

  • Primary Goal: DevOps focuses on accelerating software delivery while maintaining high quality and reliability.
  • Key Responsibilities:
    1. Continuous Integration (CI): Developers frequently merge code changes into a shared repository. Automated builds and tests ensure that new changes do not break existing software.
    2. Continuous Delivery/Deployment (CD): DevOps automates the deployment of applications to production or staging environments, reducing manual errors and increasing speed.
    3. Operations Management: DevOps ensures smooth operation of applications on servers or cloud platforms, monitoring system health, performance, and uptime.
  • Focus Area: DevOps primarily handles software applications, web services, APIs, and infrastructure, making sure they run smoothly from development to production.
  • Takeaway: DevOps is software-centric, focusing on code, infrastructure, and deployment pipelines.

MLOps

  • Primary Goal: MLOps focuses on managing the entire lifecycle of machine learning models, ensuring they can be deployed, monitored, and updated reliably in production environments.
  • Key Responsibilities:
    1. Data Collection & Preprocessing: Unlike DevOps, MLOps starts with data. It ensures data is clean, structured, and ready for training ML models.
    2. Model Training & Evaluation: MLOps handles training ML models using datasets, evaluating their accuracy, and selecting the best-performing models.
    3. Deployment of AI Models: Once a model is ready, MLOps ensures it’s deployed to production environments where it can make predictions in real time.
    4. Monitoring & Retraining: MLOps continuously monitors model performance to detect issues like data drift, accuracy drop, or bias, and retrains models when necessary.
  • Focus Area: MLOps is data and model-centric, not just code-centric. Its focus is on end-to-end ML lifecycle management, ensuring that AI models remain reliable, scalable, and effective over time.
  • Takeaway: MLOps extends DevOps principles to AI/ML, handling both data and models, not just software code.

 Key Difference in Focus:

  • DevOps: “Build, deploy, monitor software.”
  • MLOps: “Collect data → Train model → Deploy model → Monitor and retrain for accurate AI predictions.”

2. Lifecycle Management

DevOps Lifecycle Management

  • Purpose: DevOps focuses on efficiently delivering software applications from development to production. The lifecycle is generally linear and repeatable, emphasizing automation and collaboration between development and operations teams.
  • Key Stages:
    1. Code: Developers write and commit code to version control systems like Git.
    2. Build: The committed code is compiled or packaged into deployable units (e.g., executables, containers).
    3. Test: Automated tests (unit tests, integration tests, etc.) validate the code for correctness and stability.
    4. Release/Deploy: The tested code is deployed to staging or production environments using CI/CD pipelines.
    5. Operate & Monitor: Applications are continuously monitored for performance, reliability, and errors. Feedback from operations informs future development.

Characteristics of DevOps Lifecycle:

  • Linear flow from code → build → test → deploy → monitor.
  • Code-centric: Focused on software development artifacts rather than data.
  • Highly automated: CI/CD pipelines are the backbone for faster and reliable delivery.

MLOps Lifecycle Management

  • Purpose: MLOps focuses on the end-to-end lifecycle of machine learning models, which is more complex than traditional software because it involves both code and data.
  • Key Stages:
    1. Data Ingestion: Collecting raw data from multiple sources such as databases, APIs, or IoT devices.
    2. Data Preprocessing: Cleaning, normalizing, and transforming raw data into a format suitable for ML models.
    3. Feature Engineering: Extracting meaningful features from data that improve model accuracy and predictive power.
    4. Model Training: Using algorithms to train machine learning models on prepared datasets.
    5. Model Evaluation: Testing model performance using metrics like accuracy, precision, recall, and F1 score.
    6. Model Deployment: Deploying the trained model to production environments so it can make real-time predictions.
    7. Monitoring & Retraining: Continuously monitoring model performance to detect issues such as data drift, performance decay, or bias. Retraining models with updated data ensures predictions remain accurate over time.

Characteristics of MLOps Lifecycle:

  • Cyclic and iterative: Unlike DevOps, the ML lifecycle often loops back for retraining as new data comes in.
  • Data-centric: Success depends heavily on high-quality, up-to-date datasets.
  • Model-focused: Not just code deployment — the focus is on model accuracy, reliability, and performance.

 Key Takeaways:

  • DevOps: Linear, code-focused CI/CD lifecycle. Works well for software applications.
  • MLOps: Iterative, data and model-focused lifecycle. Works for machine learning systems that need continuous retraining and monitoring.

3. Data Dependency

DevOps

  • Nature of Dependency: DevOps primarily deals with software applications, so its operations depend mostly on code, configuration, and infrastructure rather than large datasets.
  • Key Points:
    1. Code-Centric: Developers focus on writing, testing, and deploying software code.
    2. Infrastructure-Centric: DevOps ensures the application runs reliably on servers, containers, or cloud platforms.
    3. Minimal Data Needs: Only small amounts of data may be required for testing, logs, or configuration files. There is no need for continuous large-scale datasets.
  • Implication: Since DevOps doesn’t rely heavily on data, data management, preprocessing, and storage are not major concerns.

MLOps

  • Nature of Dependency: MLOps revolves around machine learning models, which are entirely dependent on data quality and quantity. Without the right data, models cannot learn or make accurate predictions.
  • Key Points:
    1. Training Data: ML models need vast amounts of historical or real-time data to identify patterns and make predictions.
    2. Data Quality: Inaccurate, incomplete, or biased data can lead to poor model performance. Proper cleaning, normalization, and preprocessing are crucial.
    3. Versioning: Data must be versioned to ensure experiments are reproducible and models can be retrained reliably.
    4. Data Storage & Management: Large datasets require specialized storage solutions, data pipelines, and tools like DVC (Data Version Control) or MLflow for tracking.
    5. Continuous Updates: As new data arrives, models need retraining, meaning data pipelines must be automated and robust.
  • Implication: MLOps introduces new challenges in managing, storing, and versioning data, which are not typically part of traditional DevOps workflows.

 Key Takeaways:

  • DevOps: Code and infrastructure are the primary focus; large datasets are rarely a concern.
  • MLOps: Success depends heavily on high-quality, well-managed, and continuously updated datasets.

In short: Without proper data management, MLOps pipelines cannot function effectively, whereas DevOps pipelines can operate reliably without massive data.

4. Tooling and Technologies

DevOps

  • Purpose: DevOps relies on tools that automate software development, testing, deployment, and infrastructure management, ensuring applications are delivered quickly and reliably.
  • Common DevOps Tools:
    1. Jenkins: A CI/CD tool for automating builds, tests, and deployments. It allows developers to integrate code frequently and deploy automatically.
    2. Git: A version control system for tracking changes in source code, collaborating with multiple developers, and maintaining a history of code changes.
    3. Docker: Containerization platform that packages applications and dependencies into portable containers for consistent deployment across environments.
    4. Kubernetes: Orchestrates containerized applications, handling deployment, scaling, and management of containers in production.
    5. Ansible/Terraform: Tools for infrastructure as code (IaC), enabling automated provisioning and configuration of servers and cloud resources.

Takeaway: DevOps tools focus on code integration, automated deployment, and infrastructure management, ensuring smooth software delivery.

MLOps

  • Purpose: MLOps extends DevOps principles to machine learning workflows, adding tools to handle data, model training, deployment, and monitoring.
  • Common MLOps Tools:
    1. MLflow: Tracks experiments, manages models, and simplifies deployment of ML models. Supports model versioning and reproducibility.
    2. Kubeflow: Orchestrates end-to-end ML workflows, including training, hyperparameter tuning, and deployment on Kubernetes.
    3. TensorFlow Extended (TFX): Manages production ML pipelines, including data preprocessing, training, and model evaluation.
    4. Seldon: Deploys, scales, and monitors ML models in production environments.
    5. DVC (Data Version Control): Handles versioning of datasets and ML models, making experiments reproducible and trackable.
    6. Apache Airflow: Automates and schedules ML workflows, ensuring data pipelines run reliably.
  • Integration with DevOps Tools: MLOps still uses Jenkins, Git, Docker, and Kubernetes for CI/CD and deployment, but adds ML-specific tools to handle model lifecycle and data workflows.

Takeaway: MLOps integrates traditional DevOps tools but requires additional ML-specific platforms to manage the complexity of machine learning workflows.

 Key Takeaways:

  • DevOps: Tools automate software build, test, and deployment.
  • MLOps: Uses DevOps tools plus ML-focused platforms to manage data pipelines, model training, deployment, and monitoring.
  • Bottom line: MLOps tooling is broader and more specialized due to the need to handle both code and AI models.

5. Deployment Complexity

DevOps

  • Purpose: DevOps focuses on reliable and efficient deployment of software applications into production. The deployment process is generally well-defined and predictable.
  • Deployment Methods:
    • Containers (Docker): Packages the application and its dependencies into isolated containers, ensuring consistent behavior across environments.
    • Virtual Machines (VMs): Provides isolated environments for running applications, often used in larger enterprise setups.
    • Serverless Deployment: Functions are deployed to cloud platforms (like AWS Lambda or Azure Functions), removing the need to manage servers manually.
  • Characteristics:
    • Typically one-time deployment until the next code update.
    • Requires testing in staging environments before production deployment.
    • Focuses on system reliability, uptime, and resource efficiency.

Takeaway: DevOps deployment is predictable, code-focused, and infrastructure-oriented.

MLOps

  • Purpose: MLOps focuses on deploying machine learning models into production while maintaining their accuracy and performance over time.
  • Deployment Methods:
    • Model Serving: Deploy ML models as APIs or microservices so applications can make real-time predictions.
    • Containers & Kubernetes: Similar to DevOps, models are often containerized and orchestrated using Kubernetes for scalability.
    • Continuous Deployment: Unlike DevOps, ML models may require frequent updates as new data becomes available.
  • Additional Complexity:
    • Monitoring Model Performance: After deployment, models need constant monitoring to detect issues like data drift, concept drift, or declining accuracy.
    • Retraining Models: When the model performance drops, it must be retrained using updated datasets.
    • Rollback and Versioning: MLOps requires careful management of model versions, so outdated or underperforming models can be replaced without disrupting production.

Takeaway: MLOps deployment is dynamic and continuous, not just a one-time deployment. It combines infrastructure management with model lifecycle management.

 Key Differences in Deployment Complexity:

Aspect

DevOps

MLOps

Deployment Type

Applications & Services

ML Models & Services

Frequency

Usually per code update

Continuous, depending on model performance

Monitoring

Server & application performance

Model accuracy, data drift, and prediction quality

Challenges

Infrastructure & scaling

Model retraining, versioning, and data updates

Bottom Line: While DevOps deployment focuses on delivering software efficiently, MLOps deployment ensures ML models remain accurate and effective in real-world scenarios.

6. Monitoring and Logging

DevOps

  • Purpose: DevOps monitoring ensures that software applications and infrastructure are running smoothly, reliably, and efficiently.
  • Key Components:
    1. Application Performance Monitoring (APM): Tools like New Relic, Dynatrace, or AppDynamics track response times, throughput, and error rates of applications.
    2. Server & Infrastructure Health: Monitors CPU usage, memory, disk space, and network traffic to ensure optimal operation.
    3. Log Management: Aggregates logs from applications and servers to detect errors, failures, or unusual behavior. Tools like ELK Stack (Elasticsearch, Logstash, Kibana) or Splunk are commonly used.
    4. Alerts & Incident Management: Automated alerts notify teams of critical failures or performance drops, enabling quick remediation.

Takeaway: DevOps monitoring focuses on system performance, uptime, and operational reliability.

MLOps

  • Purpose: MLOps extends monitoring to machine learning models, tracking not only system health but also model-specific metrics that impact business outcomes.
  • Key Components:
    1. Model Accuracy Monitoring: Continuously checks if model predictions remain accurate against real-world outcomes.
    2. Data Drift Detection: Monitors changes in input data patterns that can degrade model performance over time.
    3. Bias and Fairness Checks: Ensures models are not producing biased predictions or unfair outcomes.
    4. Prediction Quality Tracking: Evaluates metrics like precision, recall, F1 score, and error rates in production.
    5. System Performance Monitoring: Tracks infrastructure metrics similar to DevOps to ensure deployed models perform efficiently.
    6. Logging & Audit Trails: Keeps records of data, model versions, and prediction results for accountability and reproducibility.

Takeaway: MLOps combines traditional DevOps monitoring with model performance monitoring, making it a dual-focus system.

 Key Differences in Monitoring and Logging:

Aspect

DevOps

MLOps

Focus

Application performance, server health

Model accuracy, data drift, prediction quality, plus system performance

Metrics Tracked

Response time, errors, resource usage

Accuracy, bias, data drift, model version, prediction errors

Tools

New Relic, Splunk, ELK Stack

MLflow, Kubeflow, Prometheus, custom monitoring scripts

Alerts

System failures

System + model performance degradation

Bottom Line: In DevOps, monitoring ensures software reliability. In MLOps, monitoring ensures both software reliability and ML model effectiveness, which is critical because even minor data changes can degrade model predictions.

7. Collaboration Between Teams

DevOps

  • Purpose: DevOps emphasizes breaking down silos between development and operations teams to accelerate software delivery.
  • Collaboration Focus:
    1. Developers: Write code, create features, and fix bugs.
    2. Operations Teams: Manage servers, deploy applications, monitor system performance, and ensure uptime.
    3. Integration: Through CI/CD pipelines, developers and operations collaborate closely, ensuring code changes move smoothly from development to production.
  • Tools for Collaboration: Git, Jira, Confluence, Slack, and CI/CD platforms foster transparency, communication, and tracking of tasks.

Takeaway: DevOps collaboration is between developers and ops teams, ensuring smooth software delivery and operational reliability.

MLOps

  • Purpose: MLOps expands collaboration to include data and ML stakeholders, because machine learning introduces data, model, and experiment dependencies.
  • Collaboration Focus:
    1. Data Scientists: Build, train, and evaluate machine learning models. Responsible for feature engineering and selecting algorithms.
    2. ML Engineers: Convert models into production-ready pipelines, handle model serving, and manage scaling.
    3. Operations Teams (Ops/DevOps): Manage infrastructure, deployment, monitoring, and CI/CD integration for models.
    4. Business Stakeholders: Provide feedback on model performance and business impact.
  • Key Difference: MLOps requires cross-functional collaboration across a larger set of roles compared to DevOps. Communication and workflow coordination are more complex, as data pipelines, model versions, and production infrastructure must all align.

Takeaway: MLOps adds data and model stakeholders into the DevOps workflow, creating a broader and more interdisciplinary collaboration ecosystem.

8. Version Control

DevOps

  • Purpose: Version control in DevOps ensures that source code is tracked, managed, and synchronized among developers. This allows teams to collaborate efficiently and maintain a history of changes.
  • Key Features:
    1. Code Tracking: Tools like Git track every change in the source code, who made it, and when.
    2. Branching and Merging: Developers can work on separate branches for new features or bug fixes and safely merge changes into the main branch.
    3. Collaboration: Multiple developers can work on the same project without overwriting each other’s code.
    4. Rollback Capability: Previous versions of code can be restored if a new change introduces bugs or errors.
  • Takeaway: In DevOps, version control is code-centric. It deals strictly with software artifacts and does not involve data or ML models.

MLOps

  • Purpose: MLOps extends version control to code, datasets, and machine learning models, making reproducibility and rollback more complex but essential for reliable ML pipelines.
  • Key Features:
    1. Code Versioning: Just like DevOps, ML pipelines and scripts are tracked using Git or similar tools.
    2. Data Versioning: Tools like DVC (Data Version Control) or Delta Lake manage dataset versions, ensuring experiments are reproducible and consistent. For example, if training data changes slightly, the system knows exactly which dataset version produced which model.
    3. Model Versioning: Tracks different trained models, their hyperparameters, and performance metrics. This allows teams to roll back to previous models if a new model underperforms.
    4. Experiment Tracking: MLOps keeps a record of experiments, including the combination of datasets, features, and algorithms used, which is critical for auditability and reproducibility.
  • Takeaway: MLOps adds model and dataset versioning to the traditional DevOps process, making version control multi-layered and more complex. This ensures that machine learning workflows are reliable, reproducible, and traceable.

 Key Differences in Version Control:

Aspect

DevOps

MLOps

Objects Tracked

Source code

Source code + Datasets + Models

Tools

Git, SVN

Git + DVC, MLflow, Kubeflow

Complexity

Low

High (data and model dependencies)

Purpose

Code collaboration & rollback

Reproducibility, model management, and experiment tracking

Bottom Line: While DevOps version control focuses only on software code, MLOps version control ensures every part of the ML workflow — data, code, and models — is tracked and reproducible, which is essential for high-quality AI systems.

9. Automation

DevOps

  • Purpose: DevOps automates software delivery and infrastructure management to speed up development cycles, reduce manual errors, and ensure consistent deployments.
  • Key Automation Areas:
    1. Build Automation: Tools like Jenkins or GitHub Actions automatically compile and build applications whenever code changes are pushed.
    2. Testing Automation: Automated unit, integration, and functional tests ensure code quality before deployment.
    3. Deployment Automation (CI/CD): Continuous Integration (CI) and Continuous Deployment (CD) pipelines automatically deploy applications to staging or production environments.
    4. Infrastructure Automation: Tools like Terraform, Ansible, or Chef automatically provision servers, networks, and cloud resources.
  • Characteristics: DevOps automation focuses on repeatable, predictable software delivery workflows. Once pipelines are set up, they run with minimal human intervention.

Takeaway: DevOps automation ensures faster, more reliable software deployment with fewer errors.

MLOps

  • Purpose: MLOps extends automation to entire ML workflows, including data, model, and deployment processes, because ML systems are data-dependent and iterative.
  • Key Automation Areas:
    1. Data Pipeline Automation: Automates data ingestion, cleaning, transformation, and feature engineering using tools like Airflow, Kubeflow Pipelines, or Prefect.
    2. Model Training Automation: Trains ML models automatically whenever new or updated data is available. Hyperparameter tuning and experiment management can also be automated.
    3. Model Testing & Validation: Automatically evaluates model performance, detects overfitting, and compares new models against production models.
    4. Deployment Automation: Automates deployment of models to production, including containerization and scaling using Docker, Kubernetes, or Seldon.
    5. Monitoring & Retraining Automation: Continuously monitors model performance, detects data drift or performance decay, and triggers retraining automatically when needed.
  • Characteristics: MLOps automation is more complex and dynamic than DevOps because it needs to handle data changes, model updates, and prediction quality continuously.

Takeaway: MLOps automation is end-to-end — from data collection to model retraining and deployment — whereas DevOps automation focuses primarily on software build, test, and deployment.

 Key Differences in Automation:

Aspect

DevOps

MLOps

Scope

Code, builds, tests, deployments

Code + Data + Models + Retraining

Trigger

Code commits

New data, model performance degradation, or code changes

Complexity

Moderate

High (due to dynamic data and model dependencies)

Tools

Jenkins, GitHub Actions, Ansible

Airflow, Kubeflow, MLflow, Docker, Seldon

Bottom Line: DevOps automation streamlines software workflows, while MLOps automation ensures continuous model lifecycle management, combining CI/CD principles with ML-specific pipelines.

Enroll For Free Demo
Whatsapp for More Details

10. Goal and Outcome

DevOps

  • Purpose: The primary goal of DevOps is to accelerate software delivery while maintaining reliability and quality. It ensures that applications are deployed quickly, efficiently, and with minimal errors.
  • Key Outcomes:
    1. Faster Software Delivery: CI/CD pipelines automate builds, testing, and deployment, reducing the time between code development and production release.
    2. Reliability: Automated testing and monitoring help catch bugs and issues before they affect end-users, ensuring stable software performance.
    3. Minimal Downtime: Infrastructure automation and continuous monitoring ensure applications remain highly available with minimal disruptions.
    4. Operational Efficiency: By bridging development and operations teams, DevOps reduces silos, improves collaboration, and streamlines workflows.

Takeaway: DevOps ensures efficient, reliable, and fast delivery of software applications, focusing on operational excellence and user satisfaction.

MLOps

  • Purpose: MLOps extends DevOps principles to the machine learning lifecycle, aiming to ensure high-quality, continuously updated AI/ML models that drive business impact.
  • Key Outcomes:
    1. Continuous Delivery of ML Models: MLOps pipelines automatically train, test, and deploy models as new data becomes available, ensuring predictions are always up-to-date.
    2. High-Quality Predictions: By monitoring model accuracy, detecting data drift, and retraining models, MLOps ensures reliable and precise predictions in production.
    3. Business Impact: Accurate ML models directly influence decision-making, customer experience, and revenue generation, making MLOps business-critical.
    4. Scalability and Reproducibility: MLOps frameworks ensure models can scale across applications and environments, while versioning maintains reproducibility of results.

Takeaway: MLOps extends DevOps beyond software delivery to AI-driven systems, ensuring ML models are continuously reliable, accurate, and impactful.

 Key Differences in Goals and Outcomes:

Aspect

DevOps

MLOps

Primary Goal

Faster, reliable software delivery

Continuous delivery of accurate ML models

Focus

Application reliability and performance

Model accuracy, predictions, and business impact

Automation

CI/CD for software

CI/CD + automated model training, retraining, and monitoring

Business Impact

Operational efficiency

AI-driven insights and decision-making

Bottom Line:

  • DevOps ensures software applications are delivered efficiently and reliably.
  • MLOps ensures ML models are delivered continuously with high-quality predictions, extending DevOps principles to the AI and machine learning world.
Enroll For Free Demo
Whatsapp for More Details

Conclusion

In today’s technology landscape, the distinction between DevOps and MLOps is becoming increasingly important. While DevOps has revolutionized software development and delivery by automating code integration, testing, and deployment, MLOps takes these principles further to manage the complex lifecycle of machine learning models.

MLOps introduces data pipelines, model versioning, automated retraining, and continuous monitoring, making it essential for businesses that rely on AI-driven products. Unlike traditional DevOps, where the focus is primarily on code and infrastructure, MLOps ensures that ML models remain accurate, scalable, and impactful, providing actionable insights and tangible business value.

For organizations looking to leverage AI effectively, understanding the differences and overlaps between DevOps and MLOps is critical. By integrating DevOps best practices with ML-specific workflows, MLOps not only accelerates deployment but also ensures high-quality predictions and continuous learning, bridging the gap between data science and operations.

What is MLOps?

MLOps, or Machine Learning Operations, is the process of managing and automating the complete machine learning lifecycle — from data collection and model training to deployment and monitoring.

DevOps is a software development methodology that combines development and operations teams to deliver software faster and more reliably using CI/CD pipelines.

DevOps focuses on software development and deployment, while MLOps focuses on machine learning model lifecycle management, including data and model operations.

MLOps is important because it helps maintain model accuracy, reliability, and scalability, ensuring ML models perform well even as data changes over time.

DevOps automates testing, building, and deployment processes, helping teams deliver high-quality software faster with fewer bugs.

The MLOps lifecycle includes data ingestion, preprocessing, model training, evaluation, deployment, monitoring, and retraining.

Common DevOps tools include Git, Jenkins, Docker, Kubernetes, Ansible, and Terraform for CI/CD and infrastructure automation.

Popular MLOps tools include MLflow, Kubeflow, TFX, Airflow, DVC, and Seldon for managing ML pipelines and models.

CI/CD stands for Continuous Integration and Continuous Delivery, enabling teams to integrate code changes frequently and deploy updates automatically.

 

In MLOps, CI/CD extends to Continuous Training (CT), ensuring ML models are retrained automatically whenever new data is available.

 

Data drift occurs when the input data distribution changes over time, causing model performance to degrade. MLOps helps detect and manage it.

 

MLOps teams include data scientists, ML engineers, and DevOps professionals working together to build and maintain ML systems.

 

Monitoring ensures that deployed ML models remain accurate, unbiased, and reliable, even as real-world data changes.

 

Yes. MLOps builds on DevOps by using tools like Git, Docker, and Kubernetes, but adds ML-specific tools for model management.

 

DevOps teams typically include software developers, QA engineers, and system administrators who collaborate on software delivery.