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€ 1.960,–
zzgl. MwSt.
€ 2.332,40
inkl. MwSt.
Buchungsnummer
33827
Veranstaltungsort
Online
1 Event
€ 1.960,–
zzgl. MwSt.
€ 2.332,40
inkl. MwSt.
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Machine Lerning Engineer
Machine Lerning Engineer
Dieser Kurs ist Bestandteil der zertifizierten Master Class "Machine Learning Engineer". Bei Buchung der gesamten Master Class sparst du über 15 Prozent im Vergleich zur Buchung dieses einzelnen Moduls.
Zur Master Class
Inhouse Training
Firmeninterne Weiterbildung nur für eure Mitarbeiter:innen - exklusiv und wirkungsvoll.
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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.
Content
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
Capstone
- 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
Benefits
- 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
Instructor
Yuri Nikulin
Matthew Millward
Methods
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
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