Current
Intermediate
40% Off
Deploy ML Models to Production
Coursera · KodeKloud · Updated March 2026
Platform rating
4.4/5
AI Tutor Rating
8.2/10
Duration
1-4 weeks
Classes
36
Learn to deploy ML models to production with AWS SageMaker covering model governance, data security, and GDPR compliance.
What you'll get
Deploy models on AWS SageMaker
Implement data governance and security
Build compliant ML deployment pipelines
Fit
Best for
Software Engineers
DevOps/MLOps Engineers
Data Engineers
Platform Engineers
Not ideal for
Learners seeking only entry-level overviews
Prerequisites & pricing
Prerequisites
Basic ML knowledge
Pricing
Subscription
Certification
Certificate
Growth Leverage: Completing this course opens pathways to roles such as Machine Learning Engineer, MLOps Engineer, or Data Scientist specializing in production deployment, allowing professionals to pursue certifications like AWS Certified Machine Learning - Specialty, enhancing their employability in a rapidly growing field.
Skills Value: Employers pay a premium for skills in deploying compliant ML models, with average salaries for MLOps Engineers reaching $120,000, as they need experts who can streamline model governance and ensure data security in line with regulations like GDPR.
AWS SageMaker
Model Deployment
Data Governance
GDPR
MLOps