Amazon SageMaker Studio helps data scientists prepare, build, train, deploy, and monitor machine learning models quickly by bringing together a broad set of capabilities purpose-built for ML. This course prepares experienced data scientists to use the tools available in SageMaker Studio to improve productivity across the entire machine learning lifecycle.
This advanced instructor-led training focuses on using Amazon SageMaker Studio in practice, including capabilities such as SageMaker Data Wrangler, Feature Store, Experiments, Debugger, Pipelines, and Model Monitor. You will also work with integrated tools such as Amazon CodeWhisperer and Amazon CodeGuru Security scan extensions. The course combines theory, demonstrations, extensive hands-on labs, and a capstone project to reinforce learning.
Course objectivesIn this course, you will learn to:
Accelerate the process of preparing, building, training, deploying, and monitoring machine learning solutions using Amazon SageMaker Studio
PrerequisitesWe recommend that all attendees of this course have:
Target audienceThis course is intended for:
Experienced data scientists who are proficient in machine learning and deep learning fundamentals
The first day covers setting up and working with Amazon SageMaker Studio, including JupyterLab extensions and the SageMaker user interface. You work with data processing using SageMaker Data Wrangler, Amazon EMR, AWS Glue interactive sessions, and SageMaker Processing. The day also introduces model development using SageMaker training jobs, built-in algorithms, custom scripts and containers, and SageMaker Experiments.
Day two continues model development with topics such as SageMaker Debugger, automatic model tuning, SageMaker Autopilot, bias detection with SageMaker Clarify, and SageMaker JumpStart. You also cover deployment and inference using SageMaker Model Registry, Pipelines, and various inference options, including scaling, testing strategies, performance, and optimisation.
The final day focuses on monitoring models using Amazon SageMaker Model Monitor and managing SageMaker Studio resources, updates, and costs. The course concludes with a capstone project where you apply what you have learned, including data preparation, feature engineering, model training and tuning, bias evaluation, batch predictions, and optional automation using SageMaker Pipelines.
Duration: 3 days
Price: 27 900 NOK
Course level: Advanced
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, diskusjoner og et avsluttende capstone-prosjekt.
Passer kurset for nybegynnere innen maskinlæring?
Nei, kurset er rettet mot erfarne data scientists med solid bakgrunn innen maskinlæring og Python.
Hvilke deler av SageMaker jobber man mest med?
Kurset dekker blant annet SageMaker Studio, Data Wrangler, Feature Store, Experiments, Debugger, Pipelines og Model Monitor.
Må jeg ha erfaring med AWS fra før?
Ja, grunnleggende AWS-kunnskap og gjennomført AWS Technical Essentials anbefales for å få fullt utbytte av kurset.
