Amazon Web Services / AWS Machine Learning & AI
Die Illustrationen sind in Kooperation von Menschen und künstlicher Intelligenz entstanden. Sie zeigen eine Zukunft, in der Technologie allgegenwärtig ist, aber der Mensch im Mittelpunkt bleibt.
KI-generierte Illustration

Amazon SageMaker Studio for Data Scientists (english)

3 days
In Kooperation mit
This advanced level course helps experienced data scientists build, train, and deploy ML models for any use case with fully managed infrastructure, tools, and workflows to reduce training time from hours to minutes with optimized infrastructure.

This course includes presentations, demonstrations, discussions, labs, and at the end of the course, you’ll practice building an end-to-end tabular data ML project using SageMaker Studio and the SageMaker Python SDK.

Module 1: Amazon SageMaker Setup and Navigation

  • Launch SageMaker Studio from the AWS Service Catalog
  • Navigate the SageMaker Studio UI
  • Demo 1: SageMaker UI Walkthrough
  • Lab 1: Launch SageMaker Studio from AWS Service Catalog

Module 2: Data Processing

  • Use Amazon SageMaker Studio to collect, clean, visualize, analyze, and transform data
  • Set up a repeatable process for data processing
  • Use SageMaker to validate that collected data is ML ready
  • Detect bias in collected data and estimate baseline model accuracy
  • Lab 2: Analyze and Prepare Data Using SageMaker Data Wrangler
  • Lab 3: Analyze and Prepare Data at Scale Using Amazon EMR
  • Lab 4: Data Processing Using SageMaker Processing and the SageMaker Python SDK
  • Lab 5: Feature Engineering Using SageMaker Feature Store

Module 3: Model Development

  • Use Amazon SageMaker Studio to develop, tune, and evaluate an ML model against business objectives and fairness and explainability best practices
  • Fine-tune ML models using automatic hyperparameter optimization capability
  • Use SageMaker Debugger to surface issues during model development
  • Demo 2: Autopilot
  • Lab 6: Track Iterations of Training and Tuning Models Using SageMaker Experiments
  • Lab 7: Analyze, Detect, and Set Alerts Using SageMaker Debugger
  • Lab 8: Identify Bias Using SageMaker Clarify

Module 4: Deployment and Inference

  • Use Model Registry to create a model group; register, view, and manage model versions; modify model approval status; and deploy a model
  • Design and implement a deployment solution that meets inference use case requirements
  • Create, automate, and manage end-to-end ML workflows using Amazon SageMaker Pipelines
  • Lab 9: Inferencing with SageMaker Studio
  • Lab 10: Using SageMaker Pipelines and the SageMaker Model Registry with SageMaker Studio

Module 5: Monitoring

  • Configure a SageMaker Model Monitor solution to detect issues and initiate alerts for changes in data quality, model quality, bias drift, and feature attribution (explainability) drift
  • Create a monitoring schedule with a predefined interval
  • Demo 3: Model Monitoring

Module 6: Managing SageMaker Studio Resources and Updates

  • List resources that accrue charges
  • Recall when to shut down instances
  • Explain how to shut down instances, notebooks, terminals, and kernels
  • Understand the process to update SageMaker Studio


  • The Capstone lab will bring together the various capabilities of SageMaker Studio discussed in this course. You will be given the opportunity to prepare, build, train, and deploy a model using a tabular dataset not seen in earlier labs. You can choose among basic, intermediate, and advanced versions of the instructions.
  • Capstone Lab: Build an End-to-End Tabular Data ML Project Using SageMaker Studio and the SageMaker Python SDK
  • Accelerating the preparation, building, training, deployment, and monitoring of ML solutions by using Amazon SageMaker Studio
  • Using the tools that are part of SageMaker Studio to improve productivity at every step of the ML lifecycle
Matthew Millward
Yuri Nikulin

This course includes presentations, demonstrations, practice labs, discussions, and a capstone project.

Who should attend

This course is intended for the following job roles:

  • Machine Learning & AI

The following course or equivalent knowledge is required: MLOps Engineering on AWS

zzgl. MwSt.
inkl. MwSt.
Ort der Veranstaltung
3 Events
Inhouse Training
Firmeninterne Weiterbildung nur für eure
Mitarbeiter:innen - exklusiv und wirkungsvoll.
In Kooperation mit
Starttermine und Details
Plätze frei
Durchführung gesichert
Durchführung fast gesichert
Plätze frei
Durchführung gesichert
Durchführung fast gesichert
Plätze frei
Durchführung gesichert
Durchführung fast gesichert
Plätze frei
Durchführung gesichert
Durchführung fast gesichert
Plätze frei
Durchführung gesichert
Durchführung fast gesichert
Plätze frei
Durchführung gesichert
Durchführung fast gesichert

The training is carried out in cooperation with an authorized training partner.
For the purpose of implementation, participant data will be transferred to the training partner and the training partner assumes responsibility for the processing of these data.
Please take note of the corresponding privacy policy.

Du hast Fragen zum Training?
Ruf uns an unter +49 761 595 33900 oder schreib uns auf service@haufe-akademie.de oder nutze das Kontaktformular.
zzgl. MwSt.