Machine Learning Engineering on AWS is an intermediate course designed for professionals who want to build, deploy, orchestrate, and operationalize machine learning solutions at scale using AWS services. The course combines theory with hands-on labs and activities to help participants develop production-ready ML applications.
This instructor-led training focuses on the full machine learning engineering lifecycle on AWS, from data preparation and model development to deployment, automation, and monitoring. Participants gain practical experience using services such as Amazon SageMaker AI and analytics tools like Amazon EMR to build scalable, robust, and operational ML solutions suitable for real-world production environments.
Course objectivesIn this course, you will learn to:
PrerequisitesWe recommend that attendees of this course have:
Target audienceThis course is designed for professionals interested in building, deploying, and operationalizing machine learning models on AWS. This includes current or aspiring machine learning engineers with limited prior AWS experience, as well as DevOps engineers, developers, and SysOps engineers.
The first day introduces machine learning on AWS, including Amazon SageMaker AI and responsible ML concepts. You analyze ML business challenges, work with data processing and exploratory data analysis, and cover data transformation and feature engineering. The day includes hands-on labs using SageMaker Data Wrangler, Amazon EMR, and SageMaker Processing.
Day two focuses on choosing modeling approaches, training ML models with Amazon SageMaker AI, evaluating and tuning models, and implementing deployment strategies. You work with built-in algorithms, SageMaker Autopilot, hyperparameter tuning, and traffic-shifting techniques through practical labs.
The final day covers securing ML resources on AWS, introducing MLOps concepts, and automating deployment with CI/CD pipelines. You also work with monitoring model performance and data quality, detecting data drift, and using SageMaker Model Monitor, supported by hands-on labs and a course wrap-up.
Duration: 3 days
Price: 27 900 NOK
Course level: Intermediate
Er dette et sertifiseringskurs?
Nei, dette er et opplæringskurs og gir ingen formell sertifisering.
Er kurset praktisk rettet?
Ja, kurset inkluderer presentasjoner, hands-on labs, demonstrasjoner og gruppeøvelser.
Hvilke AWS-tjenester brukes i kurset?
Kurset bruker blant annet Amazon SageMaker AI, SageMaker Pipelines, SageMaker Model Monitor og Amazon EMR.
Passer kurset for deltakere uten mye AWS-erfaring?
Ja, kurset er rettet mot ML-ingeniører som kan ha begrenset AWS-erfaring, men som har grunnleggende ML- og Python-kunnskap.
Dekker kurset både utvikling og drift av ML-modeller?
Ja, kurset dekker hele ML-ingeniørrollen, inkludert utvikling, automatisering, sikkerhet, drift og overvåking.
