• 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

• 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

• 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

• 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

• 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. Students will be given the opportunity to prepare, build, train, and deploy a model using a
  tabular dataset not seen in earlier labs. Students 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

A: This training is ideal for data scientists, machine learning engineers, and anyone involved in the development and deployment of machine learning models using Amazon SageMaker Studio. It is beneficial for both beginners who want to learn the basics of using SageMaker Studio and experienced practitioners who want to enhance their skills and leverage advanced features of the platform.

A: The training covers a wide range of topics, including an introduction to Amazon SageMaker Studio, data exploration and preprocessing, model development and training, hyper-parameter tuning, model deployment and monitoring, collaboration and version control, and best practices for using SageMaker Studio effectively.

A: While there are no strict prerequisites, having a basic understanding of machine learning concepts and experience with Python programming will be beneficial. Familiarity with AWS services and knowledge of Jupyter notebooks will also be advantageous.

A: To prepare for the training, it is recommended to have a good understanding of machine learning fundamentals, Python programming, and AWS services. Familiarize yourself with Jupyter notebooks and explore relevant documentation and tutorials on Amazon SageMaker Studio to gain a foundation before the training.

A: Yes, we offers online training options for the Amazon SageMaker Studio for Data Scientists course.

A: While the training provides valuable knowledge and skills in using Amazon SageMaker Studio, it does not directly prepare you for specific AWS certifications. However, it lays a solid foundation for pursuing advanced certifications related to machine learning, data science, or AWS architecture.