Curriculum
What you'll learn
Bridge the gap between model development and production operations. MLOps covers the infrastructure, automation, and governance needed to deploy, monitor, and maintain ML systems at scale — including CI/CD pipelines for models, data versioning, feature stores, and drift detection strategies.
CI/CD for ML
Data versioning
Model registries
Feature stores
Monitoring
Drift detection
After this track, you'll be able to
Design CI/CD pipelines that handle model training, validation, and deployment as first-class artifacts
Implement data versioning and lineage tracking to ensure reproducibility across experiments
Build monitoring dashboards that detect data drift, model degradation, and feature skew
Evaluate MLOps platforms and tooling against your organization's maturity level
Architect feature stores that serve consistent features across training and inference
Establish model governance workflows including approval gates, rollback procedures, and audit trails
Audience
Who this track is for
ML Engineers
Platform Engineers
Data Engineers
Engineering Managers
DevOps Engineers
By the Numbers
Why this matters now
The data behind this topic's growing importance.
$23.1B
projected global MLOps market by 2029, growing at 39% CAGR
MarketsandMarkets — MLOps Market Forecast4x
faster time-to-production for ML models at organizations with mature MLOps practices
Gartner — MLOps Maturity Model 202460%
of production ML models experience performance degradation within 6 months without proper monitoring
NeurIPS 2024 — ML Systems Workshop90%
of ML engineering effort is spent on infrastructure and operations, not model development
Google Research — Hidden Technical Debt in ML SystemsFrequently Asked Questions
Common questions
What is MLOps training, and how is it different from DevOps?
MLOps extends DevOps principles to machine learning systems, which have unique challenges: non-deterministic outputs, data dependencies, continuous retraining requirements, and model-specific monitoring needs. This training covers the infrastructure, tooling, and operational practices specifically designed for ML production systems.
Do I need to be a software engineer to benefit from MLOps training?
An engineering background helps, but this track serves multiple roles. ML engineers learn production best practices. Engineering managers learn to evaluate MLOps maturity and tooling decisions. Data scientists learn what it takes to make their models production-ready. The concepts are accessible to anyone who has completed the Machine Learning track.
Which MLOps tools and platforms does this track cover?
The track teaches platform-agnostic principles and patterns that apply across MLflow, Kubeflow, Weights & Biases, SageMaker, Vertex AI, and similar tools. Rather than teaching specific tool UIs, we focus on the architectural decisions and operational patterns that determine whether your MLOps investment succeeds.
How does MLOps training help reduce ML project failure rates?
Most ML projects fail in production, not in development. MLOps addresses the root causes: lack of reproducibility, no monitoring for model degradation, missing rollback procedures, and poor handoffs between data scientists and engineers. This track builds the operational discipline that turns promising models into reliable business systems.
Is MLOps relevant for small teams with only a few models?
Absolutely. Small teams benefit the most from MLOps fundamentals because they cannot afford production outages or model failures. Even with two or three models, proper versioning, monitoring, and deployment automation saves enormous time and prevents the most common failure modes. The track scales from small team patterns to enterprise architectures.
Keep Learning
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