Amazon SageMaker Studio for Data Scientists
Duration : 3 Days (24 Hours)
Amazon SageMaker Studio for Data Scientists Course Overview:
The Amazon SageMaker Studio for Data Scientists course is designed to assist experienced data scientists in utilizing the features and capabilities of SageMaker Studio, enabling them to enhance productivity across the entire machine learning (ML) lifecycle. With a comprehensive range of purpose-built tools, SageMaker Studio empowers data scientists to efficiently prepare, build, train, deploy, and monitor ML models.
Throughout this course, participants will gain the necessary skills to leverage SageMaker Studio effectively. They will learn how to leverage its features to streamline tasks at each stage of the ML lifecycle, from data preparation to model deployment. By utilizing SageMaker Studio’s specialized tools, data scientists can enhance their productivity and make the most of their ML projects.
By the end of the course, experienced data scientists will be well-equipped to leverage SageMaker Studio’s capabilities, enabling them to optimize their ML workflows, improve efficiency, and achieve better outcomes across the entire ML lifecycle.
Course level: Advanced
- Experienced data scientists who are proficient in ML and deep learning fundamentals. Relevant experience includes using ML frameworks, Python programming, and the process of building, training, tuning, and deploying models
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.
AWS Classroom Training
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. 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
Amazon SageMaker Studio for DataScientists Course Prerequisites:
We recommend that all students complete the following AWS course prior to attending this course:
- AWS Tech Essentials (1–day AWS instructor led course)
We recommend students who are not experienced data scientists complete the following two courses
followed by 1-year on-the-job experience building models prior to taking this course:
- The Machine Learning Pipeline on AWS (4–day AWS instructor led course)
- Deep Learning on AWS (1–day AWS instructor led course)
Q: What is the Amazon SageMaker Studio for Data Scientists training?
A: The Amazon SageMaker Studio for Data Scientists training is a comprehensive program designed to equip data scientists with the knowledge and skills to effectively utilize Amazon SageMaker Studio, a fully integrated development environment for machine learning. The training covers various aspects of using SageMaker Studio, including data exploration, model development, collaboration, and deployment.
Q: Who should consider taking the Amazon SageMaker Studio for Data Scientists training?
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.
Q: What topics are covered in the Amazon SageMaker Studio for Data Scientists training?
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.
Q: Are there any prerequisites for taking the Amazon SageMaker Studio for Data Scientists training?
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.
Q: How can I prepare for the Amazon SageMaker Studio for Data Scientists training?
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.
Q: Is the Amazon SageMaker Studio for Data Scientists training available online?
A: Yes, we offers online training options for the Amazon SageMaker Studio for Data Scientists course.
Q: Can the Amazon SageMaker Studio for Data Scientists training help in obtaining AWS certifications?
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.
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This course comes with following benefits:
- Practice Labs.
- Get Trained by Certified Trainers.
- Access to the recordings of your class sessions for 90 days.
- Digital courseware
- Experience 24*7 learner support.
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