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Master MLOps with Adaptive AI.

Master MLOps across 15 topics - Foundations, Deployment and Pipelines, and Monitoring and Scale - guided by Leyaa's adaptive intelligence.

3Parts
15Topics
96Patterns
25hEst. duration
Start Learning Free
MLOps
MLOps Course
3 - part learning journey
Beginner
Foundations
5 topics · 31 patterns
Beginner
Intermediate
Deployment and Pipelines
5 topics · 33 patterns
Intermediate
Advanced
Monitoring and Scale
5 topics · 32 patterns
Advanced
Begin Part 1: Foundations
✦ Course Structure

Your MLOps Journey in 3 Parts

Each part builds on the last - a deliberate progression from beginner to advanced.

Beginner
Beginner
Part 1: Foundations
Covers MLOps Fundamentals, Experiment Tracking, Model Versioning and Registry and 2 more topics.
5Topics
31Patterns
View topics
Intermediate
Intermediate
Part 2: Deployment and Pipelines
Covers Model Serving, Containerization for ML, ML Pipelines and 2 more topics.
5Topics
33Patterns
View topics
Advanced
Advanced
Part 3: Monitoring and Scale
Covers Model Monitoring, Automated Retraining, ML Platform Design and 2 more topics.
5Topics
32Patterns
View topics
Curriculum

Everything You'll Learn

Explore every topic and pattern in the course.

MLOps

3 parts · 15 topics · 96 patterns
Beginner
Part 1: Foundations5 topics · 31 patterns
Beginner
01
MLOps Fundamentals
6 patterns
What is MLOps
Why MLOps bridges ML research and production
MLOps vs DevOps comparison
ML lifecycle stages
Technical debt in ML systems
MLOps maturity levels
02
Experiment Tracking
7 patterns
Why experiment tracking prevents wasted work
MLflow setup and basics
Logging parameters and metrics
Logging artifacts and models
Comparing experiment runs
Weights and Biases overview
Organizing experiments with tags and notes
03
Model Versioning and Registry
6 patterns
Why model versioning enables rollback
MLflow Model Registry
Model stages (staging, production, archived)
Model metadata and lineage
Promoting models between stages
Model approval workflows
04
Data Versioning
6 patterns
Why data versioning is harder than code versioning
DVC (Data Version Control) basics
Tracking datasets with DVC
Data pipelines with DVC
Feature stores concept
Data drift detection basics
05
Reproducibility
6 patterns
Why reproducibility builds trust in ML
Environment management with conda and pip
Docker for ML reproducibility
Random seed management
Hardware and framework version tracking
Reproducible training pipelines
Intermediate
Part 2: Deployment and Pipelines5 topics · 33 patterns
Intermediate
01
Model Serving
7 patterns
Why serving architecture affects latency and cost
REST API serving with FastAPI
Batch prediction vs real-time serving
Model serialization formats (pickle, ONNX, TorchScript)
GPU vs CPU inference tradeoffs
Model optimization for serving (quantization, pruning)
A/B testing model versions
02
Containerization for ML
6 patterns
Why containers make ML deployment portable
Docker for ML workloads
Multi-stage builds for smaller images
GPU support in containers
Container registries for ML
Kubernetes for ML workloads
03
ML Pipelines
7 patterns
Why pipelines automate the ML workflow
Kubeflow Pipelines overview
Apache Airflow for ML orchestration
Pipeline components and DAGs
Parameterized pipeline runs
Pipeline scheduling and triggers
Pipeline versioning and reproducibility
04
CI/CD for ML
7 patterns
Why CI/CD differs for ML vs software
Automated testing for ML code
Data validation in CI pipeline
Model validation gates
Automated retraining triggers
Blue-green deployment for models
Canary releases for model updates
05
Feature Stores
6 patterns
Why feature stores prevent training-serving skew
Feast feature store basics
Online vs offline feature stores
Feature engineering pipelines
Feature sharing across teams
Point-in-time correctness
Advanced
Part 3: Monitoring and Scale5 topics · 32 patterns
Advanced
01
Model Monitoring
7 patterns
Why models degrade in production
Performance metric tracking
Data drift detection
Concept drift detection
Prediction distribution monitoring
Alert thresholds and policies
Evidently AI for monitoring
02
Automated Retraining
6 patterns
Why automated retraining keeps models fresh
Trigger-based retraining (schedule, drift, performance)
Training data pipeline automation
Champion-challenger model comparison
Automated model validation before promotion
Rollback strategies for failed updates
03
ML Platform Design
6 patterns
Why platforms accelerate ML team productivity
Self-service ML platform architecture
Compute resource management
Multi-tenancy and isolation
Cost allocation and optimization
Platform observability and SLAs
04
ML Governance and Compliance
7 patterns
Why governance builds trust in ML systems
Model documentation and model cards
Bias detection and fairness metrics
Explainability requirements
Audit trails for model decisions
Regulatory compliance (GDPR, AI Act)
Responsible AI practices
05
Scaling ML Systems
6 patterns
Why scaling requires different strategies
Distributed training basics
Data parallelism vs model parallelism
Auto-scaling inference endpoints
Cost optimization at scale
Multi-region deployment
Why Leyaa

Not just a course. A learning intelligence.

Leyaa doesn't just deliver content - it understands where you are, what you need, and how to get you there fastest.

Adaptive Diagnostics
Adaptive Diagnostics
Leyaa identifies which of the 96 patterns you've mastered and which need work - before you waste time on what you already know.
Pattern-Level Precision
Pattern-Level Precision
Every pattern gets a verdict - Learn, Practice, or Skip - so your study time goes exactly where it counts.
Intelligent Pacing
Intelligent Pacing
Leyaa reads your velocity across the whole course and adapts your focus list dynamically, keeping you in the optimal learning zone.
Before & After Visibility
Before & After Visibility
Every assessment creates a snapshot. Retake anytime to see exactly how much you've grown - topic by topic, pattern by pattern.
Learner Autonomy
Learner Autonomy
Leyaa recommends, never restricts. Override any suggestion and explore at your own pace - the system tracks everything regardless.
Clear Learning Path
Clear Learning Path
Foundations → Deployment and Pipelines → Monitoring and Scale - a structured journey where each part sets you up perfectly for the next.
MLOps

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Start your journey today

Start your diagnostic now. Build mastery across all 15 topics in under 15 minutes.

15 Topics
96 Patterns
Personalized roadmap
Leyaa Swift Bird