edX
edXMLOps Platforms: Amazon SageMaker and Azure ML
34 studentsBEGINNEREnglish
Korshub's take
Production machine learning requires orchestrating data, models, and infrastructure. This course covers both leading cloud platforms so you can implement end-to-end pipelines in AWS SageMaker or Azure ML—and understand why one might fit your environment better.
Comprehensive topics:
- Data engineering and ETL on cloud platforms
- Model training, tuning, and evaluation
- Deployment strategies (CPU, GPU) and scaling
- MLOps best practices for production systems
- Real-world projects and certification preparation
Ideal for data scientists, ML engineers, and cloud practitioners ready to move models beyond notebooks into production systems.
About this course
Master Cloud MLOps: AWS SageMaker & Azure ML
- Build end-to-end machine learning pipelines on leading cloud platforms
- Gain practical experience through hands-on exercises and projects
- Prepare for AWS & Azure ML certifications and job roles
Course Highlights:
- Explore data engineering & ML foundations on AWS
- Create data repos, ETL pipelines & serverless solutions
- Learn data science skills - cleaning, visualization, analysis
- Train, select & tune ML models on AWS SageMaker
- Operationalize models for production with MLOps best practices
- Deploy & maintain ML solutions using CPU/GPU instances
Ideal for data scientists, ML engineers, analysts & cloud professionals. Master comprehensive MLOps skills on AWS & Azure through real-world training.
Price
$449
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