MLOps Training Course in Chennai - MLOps Training
                     MLOps vs DevOps: Key 
Differences
Understanding the distinctions between Machine Learning Operations 
and Development Operations is crucial for modern software and data 
teams. This presentation will highlight the unique aspects of each 
discipline.
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Introduction to MLOps & DevOps
MLOps Defined DevOps Defined
Practices combining Machine Learning (ML) and A culture to automate and integrate software 
DevOps principles. development and IT operations.
Shared Goal Distinct Focus
Both aim for faster and more reliable deployments. MLOps: ML model deployment, monitoring, and 
management. DevOps: Continuous delivery and 
integration of software.
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Key Differences in Goals
MLOps Goals DevOps Goals
Improve collaboration for ML model deployment. Enhance collaboration for software delivery.
Deals with data pipelines, model monitoring. Focuses on CI/CD pipelines, automation.
Ensures ML model reproducibility in production. Prioritizes system reliability.
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Data Handling & Model Lifecycle (MLOps)
Data Versioning Model Training
Emphasizes versioning and Models are continuously trained 
managing datasets rigorously. and tested for improvement.
Feedback Loops Model Monitoring
Continuous improvement requires Models need performance 
data feedback loops. evaluation after deployment.
Code vs Model in DevOps and MLOps
Code Model
DevOps MLOps
Focuses on software code updates and bug fixes. Handles machine learning model lifecycle (training, 
tuning, retraining).
DevOps tools manage source code. MLOps tools manage datasets, models, and model versioning. Model performance 
and drift monitoring are crucial in MLOps for ongoing accuracy.
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Automation in MLOps vs DevOps
DevOps Automation
Automates software deployment and infrastructure management.
MLOps Automation
Automates the entire ML pipeline, from data to deployment.
Shared CI/CD
Both use CI/CD, but MLOps adds model validation.
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Collaboration & Roles 
in MLOps vs DevOps
DevOps Teams
Developers, IT operations, and quality assurance work together.
MLOps Teams
Data scientists, ML engineers, and software developers 
collaborate.
Integration Goals
DevOps integrates development and operations workflows. 
MLOps integrates data science with IT infrastructure.
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Testing & Validation in MLOps
DevOps Testing
Focuses on automated unit, integration, and system tests for 
code reliability.
MLOps Validation
Requires model validation, performance testing, and A/B testing 
for accuracy.
Testing data for ML models is crucial for generalization. Post-
deployment model validation is important for MLOps to ensure 
ongoing accuracy.
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Challenges in MLOps vs DevOps
Model Versioning
1 Ensuring consistent model versions across environments.
Reproducibility
2
Recreating ML experiments and results reliably.
Data Drift
3
Handling changes in data distribution over time.
Model Bias
4
Ensuring fairness and avoiding unintended biases in models.
DevOps primarily handles software deployment and scaling. MLOps also faces challenges with model retraining, monitoring 
performance, and scalability for ML-specific workflows.
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Tools in MLOps vs DevOps
CI/CD Jenkins, GitLab CI Kubeflow, MLflow
Orchestration Kubernetes, Kubeflow, Argo
Docker
Infrastructure Terraform, Ansible SageMaker, Vertex 
AI
Experiment N/A MLflow, Weights & 
Tracking Biases
DevOps tools focus on CI/CD and infrastructure automation. MLOps 
tools integrate with ML workflows (data ingestion, model training, 
deployment). Some tools like Kubernetes are common, but with 
specific use cases in each domain.
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Contact Us
MLOps Team
Address: Flat no: 205, 2nd Floor, Nilgiri Block, Aditya Enclave, Ameerpet, Hyderabad-1
Phone: +91-7032290546
Website: WWW.VISUALPATH.IN
Email: [email protected]
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THANK YOU
 Visit: www.visualpath.in
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