24 Hrs
3718+ Enrollments
Amazon SageMaker Studio for Data Scientists
Amazon SageMaker Studio is a fully integrated development environment for machine learning that provides a single place to build, train, and deploy models. This course is designed to help you master SageMaker Studio by providing hands-on experience in building, training, and deploying machine learning models using SageMaker Studio's intuitive interface.
Amazon SageMaker Studio for Data Scientists
Overview of Course
The Amazon SageMaker Studio for Data Scientists course is designed to provide you with a comprehensive understanding of Amazon SageMaker Studio and its various tools, workflows, and features. With this course, you'll learn how to use SageMaker Studio to build, train, and deploy machine learning models at scale.
Course Highlights
Learn to build, train, and deploy machine learning models at scale
Develop proficiency in data exploration and visualization
Understand how to create, train, and deploy models <br /><br />
Key Differentiators
Personalized Learning with Custom Curriculum
Training curriculum to meet the unique needs of each individual
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Trusted by over 100+ Fortune 500 Companies
We help organizations deliver right outcomes by training talent
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Flexible Schedule & Delivery
Choose between virtual/offline with Weekend options
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World Class Learning Infrastructure
Our learning platform provides leading virtual training labs & instances
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Enterprise Grade Data Protection
Security & privacy are an integral part of our training ethos
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Real-world Projects
We work with experts to curate real business scenarios as training projects
Skills You’ll Learn
Building, training, and deploying machine learning models at scale
Data exploration and visualization with Amazon SageMaker
Creating, training, and deploying models using SageMaker's built-in algorithms
Automatic model tuning and hyperparameter optimization with SageMaker
Training Options
1-on-1 Training
Access to live online classes- Flexible schedule including weekends
- Hands-on exercises with virtual labs
- Session recordings and learning courseware included
- 24X7 learner support and assistance
- Book a free demo before you commit!
Corporate Training
- Everything in 1-on-1 Training plus
- Custom Curriculum
- Extended access to virtual labs
- Detailed reporting of every candidate
- Projects and assessments
- Consulting Support
- Training aligned to business outcomes
Unlock Organizational Success through Effective Corporate Training: Enhance Employee Skills and Adaptability- Choose customized training to address specific business challenges and goals, which leads to better outcomes and success.
- Keep employees up-to-date with changing industry trends and advancements.
- Adapt to new technologies & processes and increase efficiency and profitability.
- Improve employee morale, job satisfaction, and retention rates.
- Reduce employee turnovers and associated costs, such as recruitment and onboarding expenses.
- Obtain long-term organizational growth and success.
Course Reviews
Curriculum
- 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
- 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.
Meet the instructor and learn about the course content and teaching style.- Make informed decisions about whether to enroll in the course or not.
- Get a perspective with a glimpse of what the learning process entails.
Description
Target Audience:
- Data Scientists
- Machine Learning Engineers
- Developers interested in building, training, and deploying machine learning models at scale
Prerequisite:
- Basic knowledge of Python programming language
- Basic knowledge of machine learning concepts
Benefits of the course:
- Learn from industry experts with real-world experience in using SageMaker Studio
- Hands-on experience with building, training, and deploying machine learning models using SageMaker Studio
- Gain proficiency in using SageMaker Studio's tools and workflows
- Receive a certificate upon completion of the course
Exam details to pass the course:
- There is no exam to pass this course.
- However, you will be required to complete all the assignments and projects to receive a certificate of completion.
Certification path:
- There are no specific certifications required to learn this course.
- However, it is recommended to have a basic knowledge of AWS services and machine learning concepts.
Career options after doing the course:
- Data Scientist
- Machine Learning Engineer
- AI Developer
- Cloud Solutions Architect
Why should you take this course from Skillzcafe:
Skillzcafe provides hands-on experience with real-world projects and assignments- Learn from industry experts with years of experience in using SageMaker Studio
- Flexible learning options with self-paced learning and live online classes
- Affordable pricing with no hidden fees or charges
FAQs
Amazon SageMaker Studio is a fully integrated development environment for machine learning that provides a single place to build, train, and deploy models.
Basic knowledge of Python programming language and machine learning concepts.
Yes, you will need an AWS account to access SageMaker Studio and complete the assignments and projects.
The course duration is 3 days.
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