DevOps to MLOps: A Comprehensive Transition Roadmap
Transform your DevOps expertise into valuable MLOps skills with this strategic roadmap. Learn everything from AI fundamentals to advanced LLMOps and agentic AI systems in a structured, practical approach.
Foundations of AI and Machine Learning
Key AI/ML Concepts
Master essential machine learning theory, including supervised vs. unsupervised learning paradigms, and deep learning fundamentals.
Framework Proficiency
Develop hands-on skills with industry-standard frameworks like TensorFlow and PyTorch through practical exercises.
Data Engineering
Learn critical data preprocessing techniques and feature engineering methods that transform raw data into ML-ready datasets.
Introduction to MLOps
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DevOps vs MLOps
Understand the key differences between traditional DevOps and MLOps, with emphasis on model lifecycle management and data dependencies.
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CI/CD/CT for ML
Implement continuous integration, delivery, and training workflows specifically designed for machine learning applications.
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MLOps Tooling
Explore essential tools like MLflow for experiment tracking, model versioning, and deployment orchestration in production environments.
Hands-on MLOps Projects
Batch Inference Pipeline
Build a complete pipeline for processing large datasets in batches, with automated data validation and model performance monitoring.
Real-time Inference API
Develop an API for serving predictions with low latency, implementing proper scaling, logging, and error handling.
A/B Testing Framework
Create a system for comparing model versions in production, with statistical analysis of performance metrics and automated rollback capability.
MLOps on Kubernetes
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Model Serving
KServe deployment with monitoring
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ML Pipelines
Kubeflow orchestration
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Infrastructure
Kubernetes for ML workloads
Leverage Kubernetes to orchestrate complex ML workflows at scale. Learn to deploy Kubeflow components for end-to-end ML pipelines and implement KServe for robust model serving with auto-scaling capabilities.
Cloud-based MLOps
AWS SageMaker
Build, train, and deploy ML models with integrated CI/CD, automated hyperparameter tuning, and serverless inference options.
Azure ML
Leverage Azure's end-to-end MLOps platform with powerful experiment tracking, model registry, and integration with Azure DevOps.
Google Vertex AI
Utilize Google's unified ML platform for seamless AutoML, custom training, and enterprise-grade model serving with monitoring.
LLMOps: Working with Large Language Models
Master specialized operations for large language models including prompt engineering techniques, building RAG systems for knowledge integration, and leveraging vector databases for efficient semantic search capabilities.
Future Trends: Agentic AI and Evolving MLOps
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Agentic AI Systems
Multi-agent architectures that collaborate to solve complex tasks
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AutoML Advances
Neural architecture search and automated feature engineering
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Federated Learning
Privacy-preserving ML across distributed data sources
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Ethical AI Operations
Responsible deployment with fairness and transparency
Accelerate Your Learning Journey
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Learning Paths
Structured curricula from fundamentals to advanced MLOps specialization
24/7
Support Access
Continuous expert guidance throughout your learning journey
50+
Hands-on Labs
Practical exercises in real-world MLOps scenarios
Unlock your full potential with School of DevOps comprehensive access passes. Choose from flexible plans designed to fit your learning pace and career goals.
Begin Your MLOps Transformation Today
What background knowledge is required?
Basic programming skills (Python), foundational DevOps practices, and familiarity with cloud platforms are recommended. No prior ML experience is necessary as the roadmap starts with fundamentals.
How long does the transition typically take?
Most DevOps professionals can transition to entry-level MLOps roles within 4-6 months of dedicated study. Reaching advanced proficiency typically requires 9-12 months of learning and project work.
Can I specialize in certain areas?
Absolutely! After building a foundation, you can focus on areas like cloud-specific implementations, LLMOps, or Kubernetes-based ML platforms based on your interests and career goals.