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Amazon SageMaker Studio helps data scientists to quickly prepare, create, train, deploy and monitor machine learning (ML) models. It does this by integrating a wide range of features designed specifically for ML. In this course, experienced data scientists will be trained to use the tools that are part of Amazon SageMaker Studio, including Amazon CodeWhisperer and Amazon CodeGuru Security Scan Extensions. The goal is to improve productivity at all stages of the ML lifecycle.
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Course Contents
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- Amazon SageMaker Studio Setup
- Data Processing
- Model Development
- Deployment and Inference
- Monitoring
- Managing SageMaker Studio Resources and Updates
You have access to the labs for a further 4 weeks after the course. This allows you to repeat exercises or deepen them individually.
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Target Group
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- Experienced data scientists who have mastered the basics of ML and deep learning
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Knowledge Prerequisites
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- Experience in the use of ML frameworks
- Programming experience in Python
- At least 1 year of experience as a data scientist, responsible for training, fine-tuning and deploying models
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Complementary and Continuative Courses
| Module 1: Amazon SageMaker Studio Setup |
| • JupyterLab Extensions in SageMaker Studio |
| • Demonstration: SageMaker user interface demo |
| Module 2: Data Processing |
| • Using SageMaker Data Wrangler for data processing |
| • Hands-On Lab: Analyze and prepare data using Amazon SageMaker Data Wrangler |
| • Using Amazon EMR |
| • Hands-On Lab: Analyze and prepare data at scale using Amazon EMR |
| • Using AWS Glue interactive sessions |
| • Using SageMaker Processing with custom scripts |
| • Hands-On Lab: Data processing using Amazon SageMaker Processing and SageMaker Python SDK |
| • SageMaker Feature Store |
| • Hands-On Lab: Feature engineering using SageMaker Feature Store |
| Module 3: Model Development |
| • SageMaker training jobs |
| • Built-in algorithms |
| • Bring your own script |
| • Bring your own container |
| • SageMaker Experiments |
| • Hands-On Lab: Using SageMaker Experiments to Track Iterations of Training and Tuning Models |
| • SageMaker Debugger |
| • Hands-On Lab: Analyzing, Detecting, and Setting Alerts Using SageMaker Debugger |
| • Automatic model tuning |
| • SageMaker Autopilot: Automated ML |
| • Demonstration: SageMaker Autopilot |
| • Bias detection |
| • Hands-On Lab: Using SageMaker Clarify for Bias and Explainability |
| • SageMaker Jumpstart |
| Module 4: Deployment and Inference |
| • SageMaker Model Registry |
| • SageMaker Pipelines |
| • Hands-On Lab: Using SageMaker Pipelines and SageMaker Model Registry with SageMaker Studio |
| • SageMaker model inference options |
| • Scaling |
| • Testing strategies, performance, and optimization |
| • Hands-On Lab: Inferencing with SageMaker Studio |
| Module 5: Monitoring |
| • Amazon SageMaker Model Monitor |
| • Discussion: Case study |
| • Demonstration: Model Monitoring |
| Module 6: Managing SageMaker Studio Resources and Updates |
| • Accrued cost and shutting down |
| • Updates |
| Capstone |
| • Environment setup |
| • Challenge 1: Analyze and prepare the dataset with SageMaker Data Wrangler |
| • Challenge 2: Create feature groups in SageMaker Feature Store |
| • Challenge 3: Perform and manage model training and tuning using SageMaker Experiments |
| • (Optional) Challenge 4: Use SageMaker Debugger for training performance and model optimization |
| • Challenge 5: Evaluate the model for bias using SageMaker Clarify |
| • Challenge 6: Perform batch predictions using model endpoint |
| • (Optional) Challenge 7: Automate full model development process using SageMaker Pipeline |
-
Classroom training
- Do you prefer the classic training method? A course in one of our Training Centers, with a competent trainer and the direct exchange between all course participants? Then you should book one of our classroom training dates!
-
Online training
- You wish to attend a course in online mode? We offer you online course dates for this course topic. To attend these seminars, you need to have a PC with Internet access (minimum data rate 1Mbps), a headset when working via VoIP and optionally a camera. For further information and technical recommendations, please refer to.
-
Tailor-made courses
-
You need a special course for your team? In addition to our standard offer, we will also support you in creating your customized courses, which precisely meet your individual demands. We will be glad to consult you and create an individual offer for you.
You can find the complete description of this course with dates and prices ready for download at as PDF.-
Amazon SageMaker Studio helps data scientists to quickly prepare, create, train, deploy and monitor machine learning (ML) models. It does this by integrating a wide range of features designed specifically for ML. In this course, experienced data scientists will be trained to use the tools that are part of Amazon SageMaker Studio, including Amazon CodeWhisperer and Amazon CodeGuru Security Scan Extensions. The goal is to improve productivity at all stages of the ML lifecycle.
-
Course Contents
-
- Amazon SageMaker Studio Setup
- Data Processing
- Model Development
- Deployment and Inference
- Monitoring
- Managing SageMaker Studio Resources and Updates
You have access to the labs for a further 4 weeks after the course. This allows you to repeat exercises or deepen them individually.
-
Target Group
-
- Experienced data scientists who have mastered the basics of ML and deep learning
-
Knowledge Prerequisites
-
- Experience in the use of ML frameworks
- Programming experience in Python
- At least 1 year of experience as a data scientist, responsible for training, fine-tuning and deploying models
-
Complementary and Continuative Courses
| Module 1: Amazon SageMaker Studio Setup |
| • JupyterLab Extensions in SageMaker Studio |
| • Demonstration: SageMaker user interface demo |
| Module 2: Data Processing |
| • Using SageMaker Data Wrangler for data processing |
| • Hands-On Lab: Analyze and prepare data using Amazon SageMaker Data Wrangler |
| • Using Amazon EMR |
| • Hands-On Lab: Analyze and prepare data at scale using Amazon EMR |
| • Using AWS Glue interactive sessions |
| • Using SageMaker Processing with custom scripts |
| • Hands-On Lab: Data processing using Amazon SageMaker Processing and SageMaker Python SDK |
| • SageMaker Feature Store |
| • Hands-On Lab: Feature engineering using SageMaker Feature Store |
| Module 3: Model Development |
| • SageMaker training jobs |
| • Built-in algorithms |
| • Bring your own script |
| • Bring your own container |
| • SageMaker Experiments |
| • Hands-On Lab: Using SageMaker Experiments to Track Iterations of Training and Tuning Models |
| • SageMaker Debugger |
| • Hands-On Lab: Analyzing, Detecting, and Setting Alerts Using SageMaker Debugger |
| • Automatic model tuning |
| • SageMaker Autopilot: Automated ML |
| • Demonstration: SageMaker Autopilot |
| • Bias detection |
| • Hands-On Lab: Using SageMaker Clarify for Bias and Explainability |
| • SageMaker Jumpstart |
| Module 4: Deployment and Inference |
| • SageMaker Model Registry |
| • SageMaker Pipelines |
| • Hands-On Lab: Using SageMaker Pipelines and SageMaker Model Registry with SageMaker Studio |
| • SageMaker model inference options |
| • Scaling |
| • Testing strategies, performance, and optimization |
| • Hands-On Lab: Inferencing with SageMaker Studio |
| Module 5: Monitoring |
| • Amazon SageMaker Model Monitor |
| • Discussion: Case study |
| • Demonstration: Model Monitoring |
| Module 6: Managing SageMaker Studio Resources and Updates |
| • Accrued cost and shutting down |
| • Updates |
| Capstone |
| • Environment setup |
| • Challenge 1: Analyze and prepare the dataset with SageMaker Data Wrangler |
| • Challenge 2: Create feature groups in SageMaker Feature Store |
| • Challenge 3: Perform and manage model training and tuning using SageMaker Experiments |
| • (Optional) Challenge 4: Use SageMaker Debugger for training performance and model optimization |
| • Challenge 5: Evaluate the model for bias using SageMaker Clarify |
| • Challenge 6: Perform batch predictions using model endpoint |
| • (Optional) Challenge 7: Automate full model development process using SageMaker Pipeline |
-
Classroom training
- Do you prefer the classic training method? A course in one of our Training Centers, with a competent trainer and the direct exchange between all course participants? Then you should book one of our classroom training dates!
-
Online training
- You wish to attend a course in online mode? We offer you online course dates for this course topic. To attend these seminars, you need to have a PC with Internet access (minimum data rate 1Mbps), a headset when working via VoIP and optionally a camera. For further information and technical recommendations, please refer to.
-
Tailor-made courses
-
You need a special course for your team? In addition to our standard offer, we will also support you in creating your customized courses, which precisely meet your individual demands. We will be glad to consult you and create an individual offer for you.
You can find the complete description of this course with dates and prices ready for download at as PDF.
Online Training
Trainer language German